From 8decd45db52ccdfe6f75785fe59df88df8c04d78 Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Fri, 6 Oct 2023 18:14:23 +0000
Subject: [PATCH 1/2] fix notebook

---
 .pre-commit-config.yaml |   2 +-
 model_performance.ipynb | 383 +++++++++++++++++++++++++++++++---------
 2 files changed, 299 insertions(+), 86 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2083b40..d019c73 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -16,7 +16,7 @@ repos:
   - repo: https://github.com/psf/black
     rev: 23.9.1
     hooks:
-      - id: black
+      - id: black-jupyter
   - repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.5.1
     hooks:
diff --git a/model_performance.ipynb b/model_performance.ipynb
index a89a619..fd4b244 100644
--- a/model_performance.ipynb
+++ b/model_performance.ipynb
@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn segmentationmetrics"
+    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn segmentationmetrics requests"
    ]
   },
   {
@@ -35,24 +35,44 @@
    "outputs": [],
    "source": [
     "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "from pathlib import Path\n",
     "import requests\n",
     "import zipfile\n",
     "import io\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
-    "from sklearn.metrics import cohen_kappa_score\n",
+    "import scipy.stats as stats\n",
     "import pydicom\n",
     "import pydicom_seg\n",
     "import SimpleITK as sitk\n",
+    "import matplotlib.pyplot as plt\n",
     "import segmentationmetrics as sm\n",
     "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
     "import seaborn as sns\n",
+    "\n",
     "sns.set_theme(style=\"whitegrid\")\n",
-    "colors = ['#4e67c8','#5ecbf3', '#5dceae','#a7ea53']\n",
-    "sns.set_palette(sns.color_palette(colors))\n",
-    "%matplotlib inline\n"
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt_colors = {\n",
+    "    \"ne2\": \"#5eceb0\",\n",
+    "    \"rad1\": \"#9e70e1\",\n",
+    "    \"tp\": \"k\",\n",
+    "    \"fp\": \"b\",\n",
+    "    \"fn\": \"r\",\n",
+    "}\n",
+    "\n",
+    "revewer_cmap = sns.color_palette([plt_colors[\"ne2\"], plt_colors[\"rad1\"]])\n",
+    "vol_cmap = sns.color_palette([plt_colors[\"fp\"], plt_colors[\"fn\"]])"
    ]
   },
   {
@@ -64,22 +84,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa_dir = Path(\"qa-results\")\n"
+    "qa_dir = Path(\"qa-results\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "def download_inference(\n",
     "    extract_dir=\"qa-results\",\n",
-    "    url=\"https://zenodo.org/record/8352041/files/liver-ct.zip\",\n",
+    "    url=\"https://zenodo.org/record/8400869/files/liver-ct.zip\",\n",
     "):\n",
     "    # download the zip file and extract it\n",
     "    r = requests.get(url)\n",
@@ -87,7 +107,7 @@
     "    z.extractall(extract_dir)\n",
     "\n",
     "\n",
-    "if not qa_dir.exists():\n",
+    "if not (qa_dir / \"ai-segmentations-dcm\").exists():\n",
     "    download_inference()"
    ]
   },
@@ -129,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -151,6 +171,10 @@
     "        \"LikertScore\": int,\n",
     "    },\n",
     ")\n",
+    "df.replace(\n",
+    "    {\"rad1\": \"Radiologist\", \"ne1\": \"Non-expert\"},\n",
+    "    inplace=True,\n",
+    ")\n",
     "\n",
     "# get validation subset\n",
     "val_df = df[df[\"Validation\"]]\n",
@@ -161,9 +185,9 @@
     ")\n",
     "\n",
     "# get validation done by different reviewers\n",
-    "rad_df = val_df.loc[val_df[\"Reviewer\"] == \"rad1\"]\n",
+    "rad_df = val_df.loc[val_df[\"Reviewer\"] == \"Radiologist\"]\n",
     "ne_df = val_df.loc[\n",
-    "    (val_df[\"Reviewer\"] == \"ne1\")\n",
+    "    (val_df[\"Reviewer\"] == \"Non-expert\")\n",
     "    & (val_df[\"AISegmentation\"]).isin(rad_df[\"AISegmentation\"])\n",
     "]\n",
     "\n",
@@ -180,70 +204,58 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We caluclate the Cohen-Kappa score between the two reviewers. Cohen-Kappa is a measure of agreement between raters that takes into account the possibility of the agreement occurring by chance. This tells us how much we can trust the scores given by the non-expert reviewer for the portion of the dataset the Radiologist did not review.\n",
-    "\n",
-    "Kappa value interpretation Landis & Koch (1977):\n",
+    "We caluclate the Kendall-𝜏 score between the two reviewers. Kendall-𝜏 is a measure of correlation between raters of ordinal data. This tells us how much we can trust the scores given by the non-expert reviewer for the portion of the dataset the Radiologist did not review.\n",
     "\n",
-    "| Value     | Interpretation |\n",
-    "| --------- | -------------- |\n",
-    "| <0        | No agreement   |\n",
-    "| 0 — .20   | Slight         |\n",
-    "| .21 — .40 | Fair           |\n",
-    "| .41 — .60 | Moderate       |\n",
-    "| .61 — .80 | Substantial    |\n",
-    "| .81–1.0   | Perfect        |\n"
+    "For this dataset, there is significant correlation between the two reviewers. \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cohen's Kappa: 0.45\n",
+      "Kendall's 𝜏: 0.70, p-value: 0.03\n",
       "Percentage Agreement: 66.67%\n"
      ]
     }
    ],
    "source": [
-    "rad_scores = rad_df.LikertScore.values\n",
-    "ne_scores = ne_df.LikertScore.values\n",
-    "# Calculate Cohen's Kappa\n",
-    "kappa = cohen_kappa_score(rad_scores, ne_scores, labels=[1, 2, 3, 4, 5])\n",
-    "\n",
-    "print(f\"Cohen's Kappa: {kappa:.2f}\")\n",
+    "tau, p_value = stats.kendalltau(rad_df.LikertScore.values, ne_df.LikertScore.values)\n",
+    "print(f\"Kendall's 𝜏: {tau:.2f}, p-value: {p_value:.2f}\")\n",
     "\n",
     "# Calculate percentage agreement\n",
-    "total_cases = len(rad_scores)\n",
-    "matching_cases = sum(1 for r1, r2 in zip(rad_scores, ne_scores) if r1 == r2)\n",
+    "\n",
+    "total_cases = len(rad_df.LikertScore)\n",
+    "matching_cases = np.sum(rad_df.LikertScore.values == ne_df.LikertScore.values)\n",
     "percentage_agreement = (matching_cases / total_cases) * 100\n",
     "\n",
-    "print(f\"Percentage Agreement: {percentage_agreement:.2f}%\")\n"
+    "print(f\"Percentage Agreement: {percentage_agreement:.2f}%\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f92e285ff40>"
+       "<seaborn.axisgrid.FacetGrid at 0x7feac5b4d390>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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V+++/r4CAgGwfkZLh9ddf1+OPP65OnTqpR48eqlKlis6cOaP//ve/OnnypJYuXerYNyN8HTlyRM8//7xj/X333aeNGzfK09PT6bEZbm5uGjdunAYOHKiOHTuqW7duqlChgk6dOqVt27bJ29tbH3zwQZ7PG4DCj4AGI3z++edZru/WrVuOjvf29tbMmTPVu3dv9evXT/PmzdM999yjL7/8UjExMVqzZo0+//xzlS5dWjVq1MhxeChXrpzGjh2r6dOna/To0UpLS9OcOXOuG9DGjRundevWacWKFfrjjz9kWZaqVKmiqKgoDRw40OlGhvHjx8tut2vBggWaOHGiSpYsqeDgYKeXwI8bN05+fn5avHix1q5dq7Jly2rw4MFZ3v2ZFS8vL82dO1fTp0/XypUr9e9//1ve3t6qVq2ahg0bppIlS96wjWrVqum1117TxIkTdeTIEfn5+Tke6PtXwcHBqlmzpg4fPpzjuzezU6FCBXXq1ElLlizRsWPHVLVq1Zv6PA0aNFClSpX0+++/33D0LEONGjW0cOFCTZkyRYsXL9aFCxfk6+urwMBAPfPMM077Vq9eXWXKlNHZs2edRsoy/r1OnTqZHlocFham2NhYTZ06VZ9++qmSkpJUrlw51alTRz179szNaQJwG7JZFk8+BJB/unTpolKlSmWamgUA5BzXoAHIN3v37lVcXJy6dOni6lIAoFBjBA1Anh04cED79u3TrFmzdP78ea1bt47HQwBAHjCCBiDPVq1apZdffllXr17VpEmTCGcAkEeMoAEAABiGETQAAADDENAAAAAMc9sHtP3792v//v2uLgMAACDHbvsH1V65csXVJQAAANyU234EDQAAoLAhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYxMqAtXrxYXbp0UUhIiMLCwjRgwABdvnzZ1WUBAAAUiCKuLuCvpk2bpg8//FBRUVGqV6+ezp8/ry1btigtLc3VpQEAABQIm2VZlquLyPDzzz+rU6dOmjp1qu6///58aXPv3r2SpJCQkHxpDwAA4FYzaopz0aJF8vPzy7dwBgAAUBgZFdB2796tgIAATZ06VU2aNFFwcLB69eql3bt3u7o0AACAAmPUNWinT5/W//73Px04cECvv/66vLy89MEHH6hfv35avXq1ypQpk6t2LctSUlJSPlcLALc/m83m6hIkXftz3GTFixd3dQm4zRgV0DKC1L/+9S/VqlVLklS3bl1FRETo008/1fDhw3PVbmpqquLi4vKzVAC47Xl4eCgwKEhF3N1dWsfVtDT9uG+fUlNTXVpHdkJDQ11dAm4zRgU0Hx8flS5d2hHOJKl06dIKDAzUoUOHct2uh4eHatSokR8lAsAdw2azqYi7u2bHbdPJpHiX1FCxuI/61g5TzZo1jR9FA/KTUQGtRo0aOnbsWJbbUlJSct2uzWZj+BkAculkUryOJ15waQ1eXl4u7R8oaEbdJNCqVStduHDBaTry/Pnz2rdvn4KCglxYGQAAQMExagStTZs2CgkJ0bPPPqsRI0aoaNGimjFjhjw9PfX444+7ujwAAIACYdQImpubm2bMmKF69eppzJgxev755+Xt7a158+apXLlyri4PAACgQBg1giZJvr6+evvtt11dBgAAgMsYNYIGAAAAAhoAAIBxCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYxqiAtmjRItnt9kw/77zzjqtLAwAAKDBFXF1AVj766COVLFnSsVyhQgUXVgMAAFCwjAxoQUFB8vX1dXUZAAAALmHUFCcAAAAMDWgdO3ZU7dq11bp1a02fPl1paWmuLgkAAKDAGDXFWa5cOQ0bNkx169aVzWbT+vXrNXnyZJ06dUpjxozJdbuWZSkpKSkfKwWA25/NZpOXl5ery5AkXb58WZZlubSG7PovXrx4AVaCO4FRAS08PFzh4eGO5ebNm6to0aKaPXu2oqKiVL58+Vy1m5qaqri4uPwqEwDuCF5eXgoMDHRpDSU9iik93VKxYsVcWkfa1TTt+3GfUlNTs9weGhpawBXhdmdUQMtK+/btNWvWLMXFxeU6oHl4eKhGjRr5XBkA3N5sNpurS1DxIh5yc7Np04JfdfFMiktqKFW2qMIfraKaNWu6fBQPdw7jA1p+sNlsDD8DQCF28UyKzv1+2aU1mDLdizuDkTcJ/Nny5cvl7u7u8mF2AACAgmLUCFr//v0VFhYmu90uSVq3bp3mz5+vyMhIlStXzsXVAQAAFAyjApq/v78WLlyokydPKj09XdWqVdMrr7yiPn36uLo0AACAAmNUQHv11VddXQIAAIDLGX8NGgAAwJ2GgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYxtiAdunSJbVo0UJ2u1179+51dTkAAAAFxtiANnXqVKWlpbm6DAAAgAJnZEA7fPiwPvvsMw0bNszVpQAAABQ4IwPauHHj1KtXL/n7+7u6FAAAgAJnXEBbuXKlDhw4oGeeecbVpQAAALhEEVcX8GfJycmaMGGCRowYIW9v73xr17IsJSUl5Vt7AHAnsNls8vLycnUZxkhOTpZlWVluK168eAFXg9udUQFt2rRpKlOmjB555JF8bTc1NVVxcXH52iYA3O68vLwUGBjo6jKMceTIESUnJ2e5LTQ0tICrwe3OmIB24sQJzZo1SzExMUpISJAkx6hXUlKSLl26pBIlSuSqbQ8PD9WoUSPfagWAO4HNZnN1CUbx9/e/7ggakN+MCWjHjx9XamqqBg0alGlbZGSk6tatq/nz5+eqbZvNxvAzACBPmO5FQTImoNWuXVtz5sxxWhcXF6fx48dr7NixCgkJcVFlAAAABcuYgObj46OwsLAstwUFBSkoKKiAKwIAAHAN4x6zAQAAcKczZgQtK2FhYdq/f7+rywAAAChQjKABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGCYXAe0LVu26KOPPnJat2DBArVs2VJNmzbVW2+9pbS0tDwXCAAAcKfJdUCLjo7WTz/95Fjev3+/Xn/9dfn6+qpRo0aaO3euZs6cmS9FAgAA3ElyHdAOHz6s4OBgx/KSJUvk7e2tefPmafLkyerevbuWLFmSL0UCAADcSXId0JKTk+Xt7e1Y3rRpk5o3by4vLy9JUkhIiH777be8VwgAAHCHyXVAq1Spkvbu3StJOnr0qA4ePKjmzZs7tl+8eFGenp55rxAAAOAOUyS3B3bq1EkxMTE6deqUDh06pFKlSql169aO7fv27VO1atXyo0YAAIA7Sq4DWlRUlFJTU7VhwwZVqlRJEyZMkI+PjyTpwoUL+v777xUZGZlvhQIAANwpch3QihQpohEjRmjEiBGZtpUuXVrfffddngoDAAC4U+X6GrTIyEht2bLlutu3bt3KCBoAAEAu5Dqgff/99zpz5sx1t587d07bt2/PbfMAAAB3rDy96slms11329GjR1WiRIm8NA8AAHBHuqlr0BYvXqzFixc7lqdNm6b58+dn2i8hIUH79+9XixYt8l4hAADAHeamAlpycrLOnz/vWL506ZLc3DIPwhUvXly9evXSM888k/cKAQAA7jA3FdAef/xxPf7445KkiIgIjR492unZZwAAAMi7XD9mY/369flZBwAAAP6/XAe0DImJifrtt98UHx8vy7Iybb/vvvvy2gUAAMAdJdcB7dy5cxo3bpxWr16ttLS0TNsty5LNZlNcXFyeCgQAALjT5DqgjRkzRt9884369Omjhg0bOl7zBAAAgLzJdUD77rvv1LdvX7344ov5WQ8AAMAdL9cPqi1WrJgqV66cn7UAAABAeQhoDz/8sNauXZuftQAAAEB5mOJ84IEHtH37dvXv3189e/ZUxYoV5e7unmm/oKCgPBUIAABwp8l1QMt4YK0kbd68OdN27uIEAKBwio6O1pQpU9SwYUPNmzfPadubb76pdevW8TzU6/jkk0/k7++v+++/P0/t5DqgjR8/Pk8dAwBwp0i3LLnZbIWu3x07dmjbtm0KCwvLx6pub3PmzFHLli1dF9C6du2ap44BALhTuNlsmh23TSeT4gusz4rFfdS3du6DVfHixVWjRg1NnTqVgJYDly9fVrFixfKtvVzfJAAAAHLuZFK8jideKLCf/AiDQ4YM0datW7Vr167r7nPixAk9++yzCg0NVb169dS/f3/t37/faZ+IiAj94x//0Lx589SqVSuFhoZqyJAhOnfuXI7q+M9//qPu3burTp06aty4sV5//XUlJSVJkq5evapu3bqpR48eTg/OnzFjhoKDg/XTTz9JujZtW79+fe3Zs0ePPvqoQkJC1L59e33zzTc31Z8kbdu2TXa7Xf/5z3/07LPPqkGDBho+fLgiIiJ04sQJzZs3T3a7XXa7XYsWLcrRZ/yrXI+gvfzyyzfcx2az6a233sptFwAAwIVatWqlwMBAxcTEaObMmZm2JyYmqk+fPnJzc9PYsWNVtGhRTZs2Tb1799bSpUtVqVIlx77r16/X0aNHNWbMGJ0/f17jx4/XG2+8offeey/bGlauXKkRI0aoW7duGjZsmE6fPq13331X8fHxeu+991SkSBG9/fbb6tq1qz744AM988wz+umnn/T+++/r2WefVa1atRxtpaamasSIEerXr5/8/Pz0+eefa+jQoVq0aJHsdnuO+vuz1157TQ8//LBiYmLk5uamkiVLatCgQWrQoIH69esnSapatWquzn2uA9q2bdsyrUtPT9fp06eVlpYmX19feXl55bZ5AABggKefflrDhg3Tnj17VKdOHadtixYt0m+//aavv/5a9957r6Rr7+Bu1aqVZs+erVGjRjn2tSxL06ZNk6enp6RrI2/Tp09Xenq63NyyntCzLEsTJ07UQw89pDfffNOxvly5cho0aJCGDBmimjVr6t5779Xzzz+vd955R02aNNHrr7+uOnXqaMCAAU7tpaam6umnn9ajjz4qSWrevLnatWun6dOna9KkSTnuL0NERIT+9re/OfXh6empsmXLql69ejk9xVnKdUC73t0bqampio2N1ezZszVr1qxcFwYAAFyvbdu2CggIUExMjKZPn+60bceOHY6AlKF06dJq2rSpdu7c6bTvfffd5whnknTvvfcqNTVVZ8+eVbly5ZSWlibLshzbixQpoiNHjujEiRN65ZVXdPXqVce2Ro0ayc3NTf/73/8cgalv375at26d+vbtqyJFimjp0qVZBr+2bds6/t3d3V1t2rRxPNf1ZvqTpJYtW+boHOZGrgPa9Xh4eKh37946dOiQ3njjDc2YMSO/uwAAAAXEZrMpKipKzz//vPbt2+e0LT4+XmXLls10TJkyZXTw4EGndX99Z3dGWEtJSZF0LTidOHHCsX3dunU6f/68JOmZZ57Jsrbff//dqc4OHTro+++/V0REhKpUqZJpfw8PD5UqVSpTradPn5akm+ov49hbJd8DWoZatWppyZIlN3XMhg0b9OGHH+rQoUNKTExUhQoV1KZNGw0dOlQlS5a8RZUCAIDstG/fXtHR0Zo6daruvvtux/pSpUrpyJEjmfY/e/ZspiB0I9OmTdOVK1ccy+XLl3eEtzFjxmSaXs3YJ8OpU6f03nvvKTAwUKtWrdKWLVvUpEkTp/1TU1N18eJFp9oyRvCka6N/Oe1PuhYKb5VbFtA2b95809egXbhwQXXq1FGfPn1UunRpHTx4UNHR0Tp48CDTpQAAuIibm5uioqI0atQoNWrUyLE+NDRUq1at0s8//6zq1atLki5evKjNmzerZ8+eN9VHxkX6f1a9enVVrFhRv/76q5544olsjx89erRKlSqlefPm6W9/+5teeeUVLVu2TN7e3k77rVmzxnENWlpamtauXau6devedH/X4+Hh4QiWeZHrgDZlypQs1yckJGj79u368ccfNWjQoJtqs3Pnzk7LYWFh8vT01GuvvaZTp06pQoUKuS0XAADkQadOnRQTE6Nt27apcuXKkqRu3brpk08+0eDBg/Xcc8857uIsUqSI+vbtm+c+bTabRo0apZEjRyopKUktW7aUl5eXfvvtN23YsEEjRoyQv7+/Pv/8c23evFmffvqpihcvrn/84x/q2LGjxo0bpwkTJjja8/Dw0LRp05SSkuK4i/PkyZOKiYm5qf6yU716dW3dulXfffedfHx85Ofnp7vuuuumP3u+B7RSpUqpSpUqGjt2rHr06JHb5h0yhhtTU1Pz3BYAAK5SsbjPjXcyuD93d3cNGjRIr776qmOdt7e35s6dqwkTJui1115Tenq6GjRooE8//dTpERt50b59e/n4+OiDDz7QsmXLJEmVK1dWeHi4ypYtq2PHjmnixInq37+/GjRoIOnatWFvvPGGnnnmGbVp00Zt2rSRdC2gTZo0SWPHjtWBAwfk5+en999/3+lRHDfq70aef/55/f3vf9ewYcN06dIljR8/Xt26dbvpz22z/nzLhCHS0tJ09epVHTp0SK+88oruvvtuTZs2LVdt7d27V5IUEhKSnyUCwB3jnzvX6HjiBZf0HVquip4MbKyvPjikc79fdkkNvpWKqWNUjTy1UVhf9XQ7iY6O1qxZs/TDDz+4upQcuWXXoOVFq1atdOrUKUlSeHi43n333Ty1Z1mW0xOAAaAg3MoLiG9Gbv8/3Gaz8TzLP0lOTr7uuSxevHi2x7oqJBHOCq88B7Tvv/9e//nPf/Tbb79Jku6++261bNnS6SLCmzVjxgwlJyfr0KFDmjZtmqKiovTxxx/L3d09V+2lpqYqLi4u1/UAwM3y8PBQUGCQ3Ivk7s+t/JJ2NU37ftyXq8tEvLy8FBgYeAuqKpyOHDmi5OTkLLeFhoYWcDW43eU6oF25ckUvvPCC1q5dK8uyHM83iY+P18cff6y2bdvq3XfflYeHx023nTEXXL9+fYWEhKhz585as2aNHnzwwVzV6uHhoRo18jY8DQA3w2azyb2IuzYt+FUXz+T9jq7cKFW2qMIfraKaNWvmahTNlBFAU/j7++d6NBKuN2zYMA0bNszVZeRYrgNaTEyM1qxZo379+qlfv36OC+fOnj2rWbNmaebMmYqJidFzzz2XpwLtdrs8PDx07NixXLdhs9luOPwMALfCxTMpLrt2KgPTlPmD84iClPXLr3Jg2bJl6tq1q1588UWnuxrKlCmjv/3tb+rSpYuWLl2a5wJ3796t1NRU+fn55bktAACAwiDXI2inT5/O8im7GerUqaOvv/76ptocOnSogoODZbfbVaxYMf3000+aOXOm7Ha74xZZAACA212uA1rFihX1/fff67HHHsty+/bt21WxYsWbarNOnTpavny5ZsyYIcuyVLlyZXXv3l39+/d3esEqAADA7SzXAa1Lly6Kjo5WyZIl9eSTT+qee+6RzWbTL7/8otmzZ2vlypU3fTHeoEGDbvrtAwAAALebXAe0qKgo/frrr5o/f76+/PJLubldu5wtPT1dlmWpa9euioqKyrdCAQAA7hS5Dmju7u6aMGGCnnzySW3cuFEnTpyQdO11CC1atHB6bQIAAABy7qbu4kxJSdGYMWM0d+5cx7patWpp0KBBGjt2rMaOHatBgwbp+++/19///nfenwkAQCEUHR0tu93u+AkLC9Njjz2mDRs25Ev7n3zyiex2u2N527Ztstvtjtcz5tSoUaPUsWPHfKnpr3JTU3R0tHbt2pUv/d9UQIuNjdXixYvVsmXLbPdr2bKlFi5cqC+//DIvtQEAcFtIT3fNA27z0m+xYsUUGxur2NhYvfHGG0pJSVFUVFS+BZA/CwoKUmxsrO699958bzu3clPTlClT8u1dnzc1xblixQq1a9dOVapUyXa/qlWr6sEHH9TXX3+txx9/PE8FAgBQ2Lm52Qr8rRIZb5LILTc3N9WrV8+xXLduXd1///3697//rQYNGuRDhf/H29vbqS8TuLqmmxpBO3DgQI7fN1a/fn3t378/V0UBAHC7yXirREH95HcYrFChgnx9fR3v3v7jjz/08ssvq3Xr1qpTp47atWunSZMm6cqVK07HJSYm6sUXX1T9+vXVuHFjTZw4UWlpaU77ZDWdmJKSovHjx6t58+ZOr328kf3796t///6qV6+eQkND9eyzzzpqzpCQkKCRI0eqfv36atKkiSZNmqRZs2bdcNp1wYIF6tChg+rUqeOY9t2zZ48kOY6dOHGiY2p427ZtOTm1WbqpEbTU1NQcv1vTw8Mj0y8JAAAUTpcuXdLFixcdb/Y5f/68SpcurZdfflk+Pj765ZdfFB0drdOnT2v8+PGO41555RVt2rRJI0eOlJ+fnz777DN99dVXN+xv5MiR2rRpk5577jlVr15dS5Ys0bBhwxQTE6PWrVtneczvv/+u3r17q0qVKnr77beVkpKi9957T71799bSpUvl7e0tSXr55Ze1detW/e1vf1PlypU1f/587du3L9t6tm/frtGjR6tfv366//77dfnyZe3Zs0cJCQmSrl0G1rNnT/Xp08dxXVxe3gN+UwGtfPnyOnjwYI72PXjwoMqXL5+rogAAgOtdvXpV0rXRsrffflslSpRQZGSkpGsjRi+99JJj3wYNGsjLy0ujRo3SmDFj5OXlpUOHDmn16tUaN26cHn30UUlS8+bN1a5du2z7/emnn7R69WqNHTtWvXr1kiS1aNFCJ06cyDagffLJJ7p69apmzZql0qVLS5Jq166tDh06aPHixerTp48OHTqkNWvW6J///Ke6dOkiSQoPD1f79u2zrWnPnj0qXbq002f+8zX5GdOhlSpVypep0Zua4mzatKmWLFmis2fPZrvf2bNntWTJEjVt2jRPxQEAANdISkpSUFCQgoKC1KpVK61atUoTJ05U9erVJUmWZemTTz7RQw89pDp16igoKEgjR47U1atX9euvv0qS9u7dK8uy1LZtW0e77u7uN3x9486dOyVJDz74oNP69u3b68cff1RSUlKWx+3YsUNhYWGOcCZJ9957r2rVquVoM2PK8s8hz83NTa1atcq2psDAQF24cEGjRo3Sd999p+Tk5Gz3z6ubCmgDBw5USkqK+vbtq927d2e5z+7du/Xkk08qJSVFAwYMyJciAQBAwSpWrJgWLFigL7/8Um+//bbKlSunl156SX/88Yckafbs2frnP/+p1q1ba+rUqfryyy81ZswYSdeuH5Ouvbfbw8NDpUqVcmq7TJky2fZ98eJFeXh4OAUtSSpbtqwsy3JMK/5VfHy8ypYtm2l9mTJldPHiRaeaSpYs6bSPr69vtjU1adJEEydO1MGDB9W/f381btxYL774oi5cuJDtcbl1U1OcVapU0eTJk/X888+rV69eqlKligICAlSiRAldunRJBw8e1LFjx1SsWDFNmjRJVatWvSVFAwCAW8vNzU0hISGSrr0r29/fXz169FBMTIzGjh2rlStXKiIiQi+88ILjmMOHDzu1Ua5cOaWmpurixYtOIe1GM3GlSpXK8rgzZ87IZrNlCld/Pi6rts+ePatq1ao51ZSQkODUzrlz57KtSZI6d+6szp0769y5c1q3bp3Gjx+vIkWK6K233rrhsTfrpkbQpGvzrUuXLlWPHj2UkpKitWvXasmSJVq7dq2Sk5PVvXt3LV26VBEREfleLAAAcI2QkBB16NBBixYt0unTp3X58uVMNw4uW7Ys0zGSnO6+TEtL09q1a7PtK+OJEStXrnRav3LlSgUGBqp48eLXPW7r1q2O0TJJ+vnnn7V//35Hm8HBwZKkdevWOfZJT0/XN998k21Nf+br66vu3burWbNm+vnnnx3rPTw8HKOHeZWrVz35+flp7Nixkq7dPnvp0iWVKFHCcXcEAAC4/QwZMkTLly/X7Nmz1bRpU82ZM0effvqpqlWrpqVLl+ro0aNO+9eoUUNt27bVW2+9pZSUFMddnDd601CtWrXUrl07TZgwQZcvX5a/v7+WLl2qH374QVOnTr3ucU8++aQWLVqkfv366emnn1ZKSoomT56sSpUqqWvXrpKkmjVrqm3btho3bpySk5N19913a/78+bp8+bJsNtt1237//fd14cIFNWrUSGXKlNGBAwe0adMmPfnkk459qlevrnXr1qlhw4by8vKSv79/rrNRrt/FmcHb25tgBgDADZQqW7TQ91e9enU99NBD+vzzz/Wf//xH58+f1/vvvy9JeuCBB/Tqq68qKirK6Zi33npL//jHP/TOO+/I09NTXbt2VaNGjTRx4sRs+3r77bc1adIkffjhh7pw4YKqV6+u999/P9sZukqVKmnu3LmaOHGiRo4cKTc3NzVr1kyjRo1yyioZNU2cONFRU82aNTVv3rzrth0SEqLZs2drxYoVSkxMVMWKFdW/f389/fTTjn3GjBmjt956SwMHDtTly5c1Z84chYWFZfs5r8dmWZZr3j9RQDLu1sgYZgWAgvTVB4d07vfLLunbt1IxdYzK/XOYMvxz5xodT7yQ94JyIbRcFT0Z2LjQn8f0dEtubtcfnblVXNVvYfTEE0/Izc3N6X3jrpTnETQAAJA9V4UkwlnWVq1apd9//10BAQFKTk7WV199pR07digmJsbVpTkQ0AAAwB2lePHiWrJkiX755RelpqaqevXqevvtt2/4fLaCREADAAB3lPDwcIWHh7u6jGzd9GM2AAAAcGsR0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDAENAAAAMMQ0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDAENAAAAMMQ0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMEwRVxfwZytWrNDSpUu1b98+xcfH65577lGfPn30yCOPyGazubo8AACAAmFUQPvkk09UuXJljRo1SnfddZc2b96s1157TSdPntTQoUNdXR4AAECBMCqgTZs2Tb6+vo7lJk2a6MKFC/r44481ZMgQubkxIwsAAG5/RiWeP4ezDLVr11ZiYqKSkpJcUBEAAEDBM2oELSs7d+5UhQoV5O3tnes2LMsi4KHQMOV6S8uyXF1CoWaz2eTl5eXqMiRJycnJufp9mvQZTJDdeSxevHgBV4PbndEBbceOHVq+fLleeumlPLWTmpqquLi4fKoKuHU8PDwUGBSkIu7uLq3jalqafty3T6mpqS6tozDz8vJSYGCgq8uQJB05ckTJyck3fZxJn8EE2Z3H0NDQAq4GtztjA9rJkyc1YsQIhYWFKTIyMk9teXh4qEaNGvlUGXDr2Gw2FXF31+y4bTqZFO+SGioW91Hf2mGqWbMmo2h5YMpIqCT5+/vnegQN/ye35xHIDSMDWnx8vAYOHKjSpUsrOjo6zzcH2Gw2hp9RqJxMitfxxAsurYGprdsHv8v8wXlEQTIuoF2+fFmDBw9WQkKCYmNjVbJkSVeXBAAAUKCMCmhXr17Vc889p59//lnz5s1ThQoVXF0SAABAgTMqoI0dO1bffPONRo0apcTERP33v/91bAsMDJSnp6frigMAACggRgW07777TpI0YcKETNvWrVsnPz+/gi4JAACgwBkV0NavX+/qEgAAAFzOqDcJAAAAgIAGAABgHAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhini6gL+7OjRo5o5c6Z2796tgwcPqnr16vrqq69cXRYAAECBMiqgHTx4UBs2bFDdunWVnp4uy7JcXRIAAECBM2qKMyIiQhs2bND777+voKAgV5cDAADgEkYFNDc3o8oBAABwCRIRAACAYYy6Bu1WsSxLSUlJri7jtmez2VxdgiQV6msXbTabvLy8XF2GJOny5csuP5eu7j8vTPpdJicn5+pcmvQZTJDdeSxevHgBV4Pb3R0R0FJTUxUXF+fqMm5rHh4eCgwKUhF3d5fWcTUtTT/u26fU1FSX1pFbXl5eCgwMdGkNJT2KKT3dUrFixVxaR9rVNO37kd9lfjhy5IiSk5Nv+jiTPoMJsjuPoaGhBVwNbnd3REDz8PBQjRo1XF3Gbc1ms6mIu7tmx23TyaR4l9RQsbiP+tYOU82aNQvtyIsJo5DFi3jIzc2mTQt+1cUzKS6poVTZogp/tAq/y3zi7++f6xE0/J/cnkcgN+6IgGaz2Rh+LiAnk+J1PPGCS2tgSiZ/XDyTonO/X3ZpDfwu8wfnMX9wHlGQuEkAAADAMEaNoCUnJ2vDhg2SpBMnTigxMVErV66UJDVq1Ei+vr6uLA8AAKBAGBXQzp49q+HDhzuty1ieM2eOwsLCXFEWAABAgTIqoPn5+Wn//v2uLgMAAMCluAYNAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDAENAAAAMMQ0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDAENAAAAMMQ0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDAENAAAAMMQ0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDAENAAAAMMQ0AAAAAxDQAMAADAMAQ0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAAwAAMAwBDQAAwDDGBbTDhw/rqaeeUr169dSsWTNNnDhRV65ccXVZAAAABaaIqwv4s4sXL6pv376qVq2aoqOjderUKU2YMEGXL1/WmDFjXF0eAABAgTAqoH3xxRe6dOmSpkyZotKlS0uS0tLSNHbsWA0ePFgVKlRwbYEAAAAFwKgpzo0bN6pJkyaOcCZJ7du3V3p6ur777jvXFQYAAFCAbJZlWa4uIkOTJk30yCOPaOTIkU7rw8PD1blz50zrc2LXrl2yLEseHh75VSauw2azKTE1RVetdJf0X8TmJm+PojLoK50rrj6Pnm7uKl7EU5cvXVV6mmvOpZu7TcVKFLktfpeF/TzyfczZeSxatKjsdnsBVoXbnVFTnPHx8fLx8cm0vlSpUrp48WKu2rTZbE7/xK3l7VHU1SXcFr9rE85jsRKu/+Phdvhd3g7nke/jNbfD9xGFh+u/8bdY/fr1XV0CAADATTHqGjQfHx8lJCRkWn/x4kWVKlXKBRUBAAAUPKMCWvXq1fXzzz87rUtISNDp06dVvXp1F1UFAABQsIwKaC1atNDmzZsVHx/vWLdy5Uq5ubmpWbNmLqwMAACg4Bh1F+fFixfVoUMH+fv7a/DgwY4H1Xbq1IkH1QIAgDuGUQFNuvaqpzfeeEM//PCDSpQooc6dO2vEiBHy9PR0dWkAAAAFwriABgAAcKcz6ho0AAAAENAAAACMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAc5GjR49qzJgx6ty5swIDA9WxY0dXl1QorVixQk8//bRatGihevXqqXPnzlqwYIF4eszN2bBhg3r37q3GjRsrODhYrVu31vjx47N8Ny5y7tKlS2rRooXsdrv27t3r6nIKlUWLFslut2f6eeedd1xdGlAgiri6gDvVwYMHtWHDBtWtW1fp6ekEilz65JNPVLlyZY0aNUp33XWXNm/erNdee00nT57U0KFDXV1eoXHhwgXVqVNHffr0UenSpXXw4EFFR0fr4MGDmjVrlqvLK7SmTp2qtLQ0V5dRqH300UcqWbKkY7lChQourAYoOAQ0F4mIiFCbNm0kSaNGjdL//vc/F1dUOE2bNk2+vr6O5SZNmujChQv6+OOPNWTIELm5MUicE507d3ZaDgsLk6enp1577TWdOnWKvxRz4fDhw/rss8/00ksv6fXXX3d1OYVWUFCQ03/jwJ2Cv71chOCQP7L6g7t27dpKTExUUlKSCyq6fZQuXVqSlJqa6tpCCqlx48apV69e8vf3d3UpAAohUgJuOzt37lSFChXk7e3t6lIKnbS0NKWkpGjfvn2KiYlRRESE/Pz8XF1WobNy5UodOHBAzzzzjKtLKfQ6duyo2rVrq3Xr1po+fTpTxrhjMMWJ28qOHTu0fPlyvfTSS64upVBq1aqVTp06JUkKDw/Xu+++6+KKCp/k5GRNmDBBI0aM4H8S8qBcuXIaNmyY6tatK5vNpvXr12vy5Mk6deqUxowZ4+rygFuOgIbbxsmTJzVixAiFhYUpMjLS1eUUSjNmzFBycrIOHTqkadOmKSoqSh9//LHc3d1dXVqhMW3aNJUpU0aPPPKIq0sp1MLDwxUeHu5Ybt68uYoWLarZs2crKipK5cuXd2F1wK3HFCduC/Hx8Ro4cKBKly6t6OhorvHLpVq1aql+/frq3r27pk6dqm3btmnNmjWuLqvQOHHihGbNmqVnn31WCQkJio+Pd1wLmZSUpEuXLrm4wsKtffv2SktLU1xcnKtLAW45RtBQ6F2+fFmDBw9WQkKCYmNjnW7JR+7Z7XZ5eHjo2LFjri6l0Dh+/LhSU1M1aNCgTNsiIyNVt25dzZ8/3wWVAShsCGgo1K5evarnnntOP//8s+bNm8fjIPLR7t27lZqayk0CN6F27dqaM2eO07q4uDiNHz9eY8eOVUhIiIsquz0sX75c7u7uCgwMdHUpwC1HQHOR5ORkbdiwQdK1aZHExEStXLlSktSoUSOe+5NDY8eO1TfffKNRo0YpMTFR//3vfx3bAgMD5enp6briCpGhQ4cqODhYdrtdxYoV008//aSZM2fKbrc7nteHG/Px8VFYWFiW24KCghQUFFTAFRVe/fv3V1hYmOx2uyRp3bp1mj9/viIjI1WuXDkXVwfcegQ0Fzl79qyGDx/utC5jec6cOdf9Qx7OvvvuO0nShAkTMm1bt24doz85VKdOHS1fvlwzZsyQZVmqXLmyunfvrv79+xNy4RL+/v5auHChTp48qfT0dFWrVk2vvPKK+vTp4+rSgAJhs3jHEAAAgFG41Q0AAMAwBDQAAADDENAAAAAMQ0ADAAAwDAENAADAMAQ0AAAAwxDQAAAADENAA1zk+PHjstvtWrRokWPdqFGjVL9+fRdWBQAwAQENuAUWLVoku92uvXv3urqULB06dEjR0dE6fvx4jo/ZsWOHBgwYoPDwcIWEhKhly5aKiorSsmXLbmGlAHBn4lVPgItUrlxZe/bsUZEiBf+f4aFDhzRlyhQ1atQoR6/DWrFihUaMGKHatWsrMjJSpUqV0vHjx7V9+3bNnz9fnTp1KoCqAeDOQUADXMRms6lo0aIF2mdKSoo8PDxu+rgpU6aoRo0aio2NzfRuzrNnz+ZXeTdkWZZSUlJUrFixAusTAFyBKU7ARbK6Bi0rcXFxaty4sfr06aNLly5Jkk6dOqWXX35ZTZs2VXBwsDp06KAFCxY4Hbdt2zbZ7XZ9/fXXeu+99xQeHq66detqzpw5Gj58uCQpMjJSdrtddrtd27Ztu24Nx44dU0hISJYvTi9TpozTcnp6umbPnq1OnTopJCREjRs3Vv/+/Z2me69evaqYmBi1adNGwcHBioiI0KRJk3TlyhWntiIiIjR48GBt2rRJ3bp1U506dfTFF19IkuLj4/Xmm2/q/vvvV3BwsNq2basZM2YoPT092/MJAIUBI2iAwfbs2aMBAwYoODhYU6dOVbFixXTmzBn16NFDNptNTzzxhHx9fbVx40aNHj1aiYmJevLJJ53amDp1qjw8PNS/f39duXJFzZs3V58+fTR37lxFRUWpevXqkqR77733unXcfffd2rJli06ePKmKFStmW/Po0aO1aNEitWjRQo8++qjS0tK0Y8cO7d69WyEhIZKkV199VYsXL9YDDzygp556Snv27NH06dN1+PBhxcTEOLV35MgRvfDCC+rZs6d69Oghf39/JScnq3fv3jp16pR69eqlSpUq6YcfftCkSZN0+vRpjR49OhdnGwDMQUADDLVz504NGjRIDRs2VHR0tGP06r333lNaWpqWLVumu+66S5L02GOP6fnnn9eUKVPUq1cvpynAlJQULVy40Gldw4YNNXfuXDVt2lRhYWE3rGXgwIEaPXq02rRpowYNGig0NFTNmjVTgwYN5Ob2fwPxW7du1aJFi9SnTx+9+uqrjvX9+vWTZVmSpJ9++kmLFy9W9+7dNW7cOElyBM1Zs2Zp69ataty4sePYo0eP6qOPPlJ4eLhj3dSpU/Xrr79q8eLFqlatmiSpV69eKl++vGbOnKl+/fqpUqVKOT7XAGAapjgBA23dulUDBgxQkyZNnMKZZVlavXq1IiIiZFmWzp075/hp3ry5EhIStG/fPqe2unTpkudrth599FF99NFHCgsL065duzR16lQ98cQTateunXbt2uXYb/Xq1bLZbBo6dGimNmw2myRpw4YNkqSnnnrKaXu/fv2ctmfw8/NzCmeStHLlSoWGhsrHx8fpHDRt2lRpaWnavn17nj4vALgaI2iAYVJSUjR48GAFBQVp8uTJTnd5njt3TvHx8YqNjVVsbGyWx587d85pOSd3aeZEeHi4wsPDlZycrH379mn58uX64osvFBUVpRUrVqhMmTI6duyYypcvr9KlS1+3nRMnTsjNzU1Vq1Z1Wl+uXDn5+PjoxIkTN6z/6NGj2r9/v5o0aZJlH389BwBQ2BDQAMN4enqqRYsWWr9+vTZt2qRWrVo5tmVcAP/www+ra9euWR5vt9udlvP7jkcvLy81bNhQDRs21F133aUpU6Zo48aN163nejJG1G4kq/rT09PVrFkzDRgwIMtjMqY9AaCwIqABhrHZbHrnnXc0ZMgQDR8+XB9++KHjOjFfX1+VKFFC6enpatq0aZ76yA/BwcGSpNOnT0uSqlatqm+//VYXLly47iha5cqVlZ6erqNHjzrdmHDmzBnFx8ercuXKN+y3atWqSkpKytM5AACTcQ0aYCBPT09NmTJFISEhioqK0p49eyRJ7u7ueuCBB7Rq1SodOHAg03E5ndrz8vKSJCUkJORo/y1btmS5PuN6MX9/f0lSu3btZFmWpkyZkmnfjJsE7r//fknS7NmznbZ//PHHTtuz0759e/3www/atGlTpm3x8fG6evXqDdsAAJMxggbcQgsXLswyRERGRt7w2GLFimn69OmKjIzUwIEDNXfuXAUEBOiFF17Qtm3b1KNHD3Xv3l01atTQxYsXtW/fPm3ZskXff//9DduuXbu23N3d9eGHHyohIUGenp5q3LhxpmeaZRgyZIj8/PzUqlUrValSRcnJydq8ebO++eYbhYSEOKZhGzdurM6dO2vu3Lk6evSowsPDlZ6erp07dyosLEy9e/dWrVq11LVrV8XGxio+Pl733Xef9u7dq8WLF6tNmzZOd3BeT//+/bV+/XpFRUWpa9euCgoKUnJysg4cOKBVq1Zp3bp18vX1vWE7AGAqAhpwC33++edZru/WrVuOjvf29tbMmTPVu3dv9evXT/PmzdM999yjL7/8UjExMVqzZo0+//xzlS5dWjVq1NDIkSNz1G65cuU0duxYTZ8+XaNHj1ZaWprmzJlz3YA2btw4rVu3TitWrNAff/why7JUpUoVRUVFaeDAgU43MowfP152u10LFizQxIkTVbJkSQUHBzu9BH7cuHHy8/PT4sWLtXbtWpUtW1aDBw/O8u7PrHh5eWnu3LmaPn26Vq5cqX//+9/y9vZWtWrVNGzYMJUsWTJH7QCAqWxWxrwDAAAAjMA1aAAAAIYhoAEAABiGgAYAAGAYAhoAAIBhCGgAAACGIaABAAAYhoAGAABgGAIaAACAYQhoAAAAhiGgAQAAGIaABgAAYBgCGgAAgGEIaAAAAIb5fxJfY9ZVeSZdAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 740.75x600 with 1 Axes>"
+       "<Figure size 640.75x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -258,8 +270,6 @@
     "            df2.loc[(reviewer, score), \"SeriesInstanceUID\"] = 0\n",
     "\n",
     "df2.reset_index(inplace=True)\n",
-    "# change rad1 to radiologist\n",
-    "df2.replace({\"rad1\": \"Radiologist\", \"ne1\": \"Non-expert\"}, inplace=True)\n",
     "\n",
     "g = sns.catplot(\n",
     "    data=df2,\n",
@@ -267,15 +277,51 @@
     "    x=\"LikertScore\",\n",
     "    y=\"SeriesInstanceUID\",\n",
     "    hue=\"Reviewer\",\n",
-    "    # palette=\"dark\",\n",
-    "    # alpha=0.6,\n",
-    "    height=6,\n",
     "    orient=\"v\",\n",
+    "    palette=revewer_cmap,\n",
     ")\n",
     "# g.despine(left=True)\n",
     "g.set_axis_labels(\"Likert Score\", \"Counts\")\n",
     "g.legend.set_title(\"\")\n",
-    "g.set(title=f\"Likert Score by Reviewer\\nCohen's Kappa: {kappa:.2f}\")"
+    "g.set(title=f\"Likert Score by Reviewer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Likert Score Distribution for Radiologist')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_df = (\n",
+    "    rad_df[\"LikertScore\"].value_counts().reset_index().sort_values(by=\"LikertScore\")\n",
+    ")\n",
+    "plt.pie(\n",
+    "    pie_df[\"count\"].values,\n",
+    "    labels=pie_df[\"LikertScore\"].values,\n",
+    "    labeldistance=0.9,\n",
+    ")\n",
+    "plt.title(\"Likert Score Distribution for Radiologist\")"
    ]
   },
   {
@@ -293,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,15 +417,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "add_metrics_to_df(rad_df)\n",
     "add_metrics_to_df(ne_df)\n",
     "\n",
-    "xdf = pd.concat([rad_df, ne_df])\n",
-    "xdf.replace({\"rad1\": \"Radiologist\", \"ne1\": \"Non-expert\"}, inplace=True)\n"
+    "xdf = pd.concat([rad_df, ne_df])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dice</th>\n",
+       "      <th>hausdorff_distance_95</th>\n",
+       "      <th>mean_surface_distance</th>\n",
+       "      <th>mean_surface_distance_tol_7</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reviewer</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Non-expert</th>\n",
+       "      <td>0.970476</td>\n",
+       "      <td>5.574182</td>\n",
+       "      <td>0.869856</td>\n",
+       "      <td>0.951877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Radiologist</th>\n",
+       "      <td>0.970285</td>\n",
+       "      <td>5.662749</td>\n",
+       "      <td>0.814795</td>\n",
+       "      <td>0.956100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 dice  hausdorff_distance_95  mean_surface_distance  \\\n",
+       "Reviewer                                                              \n",
+       "Non-expert   0.970476               5.574182               0.869856   \n",
+       "Radiologist  0.970285               5.662749               0.814795   \n",
+       "\n",
+       "             mean_surface_distance_tol_7  \n",
+       "Reviewer                                  \n",
+       "Non-expert                      0.951877  \n",
+       "Radiologist                     0.956100  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xdf[\n",
+    "    [\n",
+    "        \"Reviewer\",\n",
+    "        \"dice\",\n",
+    "        \"hausdorff_distance_95\",\n",
+    "        \"mean_surface_distance\",\n",
+    "        \"mean_surface_distance_tol_7\",\n",
+    "    ]\n",
+    "].replace([np.inf, -np.inf], np.nan).dropna().groupby(\"Reviewer\").mean()"
    ]
   },
   {
@@ -391,22 +523,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f91600e7d30>"
+       "<seaborn.axisgrid.FacetGrid at 0x7feac5d371c0>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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sPvzwQ82cOfOGY8eMGaOvvvpKf/rTn/TXv/5Vrq6uio6O1qlTp5z6vfDCC9q2bZsmT56sP//5zzp69KieffZZXb58+VbtFgAAN1Stogu42vvvv68LFy5o/vz5qlOnjiSpoKBAU6ZMUUxMjHx8fK457vvvv9eWLVu0cOFCdevWTZIUFhamhx9+WEuWLNGkSZMkSd999522bt2qJUuWqHPnzpIkX19fRUZG6ssvv1RkZOSt30kAAK7BqDPkLVu2qFOnTo4wlqSIiAgVFhZq27ZtxY776aefZLPZFB4e7mirUaOGOnbsqG+++cZpfrvd7tTPz89PrVu31pYtW8p3ZwAAKAWjAjk5OVl+fn5ObXa7Xd7e3kpOTi52XF5enlxcXOTq6urU7ubmppSUFOXk5Djm9/X1lc1mc+rn5+d33fkBALjVjFqyzszMlN1uL9Lu5eWljIyMYsc1b95cBQUF+umnnxQUFCRJKiws1P79+2VZljIzM1W9enVlZmaqdu3a15x///79Za7bsixdvHixzOMBAFWXp6dnifoZFchlFR4ermbNmunVV1/VrFmzVL9+fb311ls6ceKEJBU5Iy5v+fn517yjGwCAkJCQEvUzKpDtdruysrKKtGdkZMjLy6vYce7u7pozZ47Gjh2r3r17S5L8/f0VHR2tlStXOq5J2+12nT59utTz34ibm5tatWpV5vEAABgVyNe6lpuVlaWzZ88Wubb8n9q1a6eEhAQdP35clmWpRYsWmjp1qtq2bSs3NzfH/Dt27JBlWU5nzUePHpW/v3+Z67bZbCVekgAA4FqMuqmrS5cu2r59uzIzMx1tCQkJcnFxcbozujg2m00tWrSQr6+v0tLStG7dOg0YMMBp/oyMDO3YscPRdvToUf3000/q0qVL+e4MAAClYLMsy6roIq7IyMhQVFSUfH19FRMTozNnzmjmzJnq3bu34uLiHP2io6OVmpqqDRs2ONoWLlyo5s2bq379+jp69KgWL14sPz8/vf3223Jx+fffHUOHDtWRI0f00ksvycPDQ3PmzJGLi4s+/vhjVatW+gWDxMRESVJgYOBN7DkA4HZn1JK1l5eXli9frmnTpmnEiBGqWbOmHn30UcXGxjr1KywsVEFBgVNbZmamZs2apXPnzqlhw4bq06ePhg8f7hTGkvSXv/xFM2bMUFxcnC5fvqzOnTtr0qRJZQpjAADKi1FnyJURZ8gAgPJg1DVkAABuVwQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBggFIH8rFjxxQYGKj4+Pjr9ps1a5aCgoIcD3gAAADFK3Ugr1y5Ut7e3kU+rOM/xcbGqkGDBlq5cmWZiwMA4HZR6kDeunWrIiMjHQ9sKI67u7uioqK0ZcuWMhcHAMDtotSBfOrUKfn6+paob/PmzZWamlrqogAAuN2UOpDd3d118eLFEvW9dOnSDc+kAQBAGQLZz89P27dvL1HfHTt2qGXLlqUuCgCA202pAzkyMlKbNm3SV199dd1+X331lTZt2qTIyMgyFwcAwO2i1E97ysvL02OPPaakpCQ9+uij6tOnjwICAlSzZk1duHBBSUlJWrNmjf7+978rICBA77//vtzd3W9V/RWOpz0BAMpDmR6/mJaWpvHjx2vz5s2y2WxFfm5Zlh544AHNmjVL9erVK5dCTUUgAwDKw009D3nfvn3auHGjkpOTlZ2drVq1asnPz09du3ZVcHBwOZZpLgIZAFAebiqQQSADAMpHtfKc7Pz589qyZYvOnj0rX19fdevWTS4ufFw2AAA3UupA/uyzz/TRRx9pzpw5TteHv/vuOz333HPKzMyUZVmy2WwKCgrS3/72N3l6epZr0QAAVDWlPn397LPPdPnyZacwtixLL774orKzszVixAgtWrRIgwYN0g8//KB33nmnXAsGAKAqKvUZclJSkvr06ePUtnfvXp04cUJPPvmknn/+eUnSQw89pNOnT2vDhg0aNWpU+VQLAEAVVeoz5HPnzqlJkyZObdu2bZPNZlNERIRTe3h4OI9fBACgBEodyHXq1FFmZqZT2549e1StWjW1a9fOqb1GjRrXfJ8yAABwVupADggI0Oeff67Lly9Lks6cOaO9e/cqLCxMHh4eTn1PnDihhg0blk+lAABUYaW+hhwTE6PBgwerX79+CgwM1M6dO3X58mU9/fTTRfp+/fXXRc6aAQBAUaU+Q+7YsaPeeOMNFRYW6rPPPpO7u7tee+01hYeHO/XbsWOHTp48qYcffrjcigUAoKrik7puEp/UBQAoD2X+pK68vDx9+umn2rZtm3755RdduHBBNWvWVPPmzfXAAw+oV69eVfopTwAAlKcynSEnJSVp+PDhSk1NlWVZql27tjw9PXXx4kVlZWXJZrOpadOmWrhwoVq2bHkr6jYGZ8gAgPJQ6kC+cOGCevfurfPnz2v48OHq27evfHx8HD8/c+aM/vGPf2jhwoXy9vbWp59+WqU/OpNABgCUh1Lf1LV69WqdOnVKixcv1rBhw5zCWJJ8fHwUExOjhQsX6uTJk/rkk0/KrVgAAKqqUgfypk2bFB4errCwsOv269Spk+6//359/fXXZS4OAIDbRakD+eDBgwoNDS1R3/vuu08HDx4sdVEAANxuSh3IGRkZ8vb2LlHfBg0aKCMjo9RFAQBwuyl1IOfl5alatZK9W8rV1VX5+fmlLgoAgNtNmd6HnJKSoh9//PGG/U6ePFmW6QEAuO2U+m1Pd999d4mf4GRZlmw2mw4cOFCm4ioD3vYEACgPpT5DnjFjxq2oAwCA21qpA7lfv363og4AAG5rpb6pCwAAlD8CGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYwLpCPHDmip59+WsHBwQoPD1d8fLzy8vJuOC4tLU1xcXF66KGHFBwcrF69emnVqlVOfXbt2qWAgIAiX7GxsbdqdwAAKJFqFV3A1TIyMhQdHa0WLVpo3rx5OnPmjGbOnKmcnBzFxcVdd+zo0aOVnJysMWPGqFGjRtqyZYsmT54sV1dXDRw40KnvjBkz5Ofn5/i+bt26t2R/AAAoKaMC+f3339eFCxc0f/581alTR5JUUFCgKVOmKCYmRj4+Ptccd/bsWe3atUszZsxQ//79JUmdOnVSYmKiPv/88yKBfNdddykwMPCW7gsAAKVh1JL1li1b1KlTJ0cYS1JERIQKCwu1bdu2YsddvnxZklS7dm2n9lq1asmyrFtSKwAA5cmoQE5OTnZaSpYku90ub29vJScnFzuuUaNG6ty5sxYtWqTDhw8rOztb69at07Zt2/TEE08U6T9s2DC1bt1aXbp00axZs5STk1Pu+wIAQGkYtWSdmZkpu91epN3Ly0sZGRnXHTtv3jzFxsYqKipKkuTq6qpJkybpkUcecfSpXbu2nnnmGd17773y8PDQzp07tXTpUiUnJ2vx4sVlrtuyLF28eLHM4wEAVZenp2eJ+hkVyGVlWZYmTJigY8eOafbs2fL29tb27ds1ffp0eXl5OUK6TZs2atOmjWNcp06d1LBhQ02dOlX79u1TUFBQmbafn5+vAwcOlMu+AACqlpCQkBL1MyqQ7Xa7srKyirRnZGTIy8ur2HGbNm1SQkKC1qxZo4CAAElSWFiYzp07p5kzZzoC+VoiIiI0depU7d+/v8yB7ObmplatWpVpLAAAkmGB7OfnV+RacVZWls6ePVvk2vLVDh8+LFdXV/n7+zu1t27dWh999JEuXbqkGjVq3JKaJclms5V4SQIAgGsx6qauLl26aPv27crMzHS0JSQkyMXFReHh4cWOu/POO1VQUKCkpCSn9h9//FH169e/bhh//vnnksTboAAAFcqoM+THHntMK1eu1IgRIxQTE6MzZ84oPj5ejz32mNN7kKOjo5WamqoNGzZI+i3IGzdurFGjRmnEiBFq2LChtm7dqk8++UQjR450jBs3bpyaN2+uNm3aOG7qWrZsmbp3704gAwAqlM0y7I26R44c0bRp0/Tdd9+pZs2a6tu3r2JjY+Xu7u7oM3jwYKWkpOjrr792tB0/flxz5szRnj17lJWVpSZNmmjAgAF68skn5erqKklavHix1q5dq5SUFOXn5+vOO+9U7969NWzYMKf5SyMxMVESZ9gAgJtjXCBXNgQyAKA8GHUNGQCA2xWBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYADjAvnIkSN6+umnFRwcrPDwcMXHxysvL++G49LS0hQXF6eHHnpIwcHB6tWrl1atWlWk35kzZzRy5Ei1b99eoaGhevnll5WdnX0rdgUAgBKrVtEFXC0jI0PR0dFq0aKF5s2bpzNnzmjmzJnKyclRXFzcdceOHj1aycnJGjNmjBo1aqQtW7Zo8uTJcnV11cCBAyVJ+fn5euaZZyRJs2fPVk5OjmbNmqWxY8dq8eLFt3z/AAAojlGB/P777+vChQuaP3++6tSpI0kqKCjQlClTFBMTIx8fn2uOO3v2rHbt2qUZM2aof//+kqROnTopMTFRn3/+uSOQ169fr0OHDmndunXy8/OTJNntdg0dOlT79u1TUFDQrd9JAACuwagl6y1btqhTp06OMJakiIgIFRYWatu2bcWOu3z5siSpdu3aTu21atWSZVlO8wcEBDjCWJLCw8NVp04dbd68uZz2AgCA0jMqkJOTk53CUvrtDNbb21vJycnFjmvUqJE6d+6sRYsW6fDhw8rOzta6deu0bds2PfHEE9ed32azydfX97rzAwBwqxm1ZJ2ZmSm73V6k3cvLSxkZGdcdO2/ePMXGxioqKkqS5OrqqkmTJumRRx5xmv8/z6JLOv/1WJalixcvlnk8AKDq8vT0LFE/owK5rCzL0oQJE3Ts2DHNnj1b3t7e2r59u6ZPny4vLy9HSN8q+fn5OnDgwC3dBgCgcgoJCSlRP6MC2W63Kysrq0h7RkaGvLy8ih23adMmJSQkaM2aNQoICJAkhYWF6dy5c5o5c6YjkO12+zXf4pSRkaFGjRqVuW43Nze1atWqzOMBADAqkP38/Ipcy83KytLZs2eLXPu92uHDh+Xq6ip/f3+n9tatW+ujjz7SpUuXVKNGDfn5+engwYNOfSzL0tGjRxUeHl7mum02W4mXJAAAuBajburq0qWLtm/frszMTEdbQkKCXFxcrhuYd955pwoKCpSUlOTU/uOPP6p+/fqqUaOGY/6ff/5Zx44dc/TZsWOH0tPT9eCDD5bvzgAAUAo26+r3BVWwjIwMRUVFydfXVzExMY4PBundu7fTB4NER0crNTVVGzZskCRlZ2erd+/ecnNz04gRI9SwYUNt3bpVS5cu1ciRIzV8+HBJv13rvfI+5TFjxujSpUuKj49XQEBAmT8YJDExUZIUGBh4M7sOALjNGRXI0m8fnTlt2jR99913qlmzpvr27avY2Fi5u7s7+gwePFgpKSn6+uuvHW3Hjx/XnDlztGfPHmVlZalJkyYaMGCAnnzySbm6ujr6nTlzRq+99pq2bt2qatWqqUePHpo4caJq1apVpnoJZABAeTAukCsbAhkAUB6MuoYMAMDtikAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEBGlbB3716NHj1ae/furehSAKBMCGRUerm5uVq6dKl+/fVXLV26VLm5uRVdEgCUGoGMSm/NmjVKT0+XJKWnp2vt2rUVWxAAlAGBjErt9OnTWrt2rSzLkiRZlqU1a9bo9OnTFVwZAJQOgYxKy7IsLVu2zBHGN2oHAJMRyKi0UlNTlZiYqMLCQqf2wsJCJSYmKjU1tYIqA4DSI5BRaTVu3FiBgYFycXH+NXZxcVFQUJAaN25cQZUBQOkRyKi0bDabnnrqKdlsthK1A4DJCGRUanfccYd69+7tCF+bzaY+ffrIx8engisDgNIhkFHp9enTR3Xq1JEk1a1bV717967YggCgDAhkVHoeHh4aMmSIGjRooKeffloeHh4VXRIAlJrN4r0hNyUxMVGSFBgYWMGVAAAqM86QAQAwAIEMAIABCGQAAAxAIANAFcQjSSsfAhkAqhgeSVo5EcgAUMXwSNLKiUAGgCqER5JWXgQyAFQRPJK0ciOQAaCK4JGklRuBDABVBI8krdwIZACoIngkaeVGIANAFcIjSSsvAhkAqhgeSVo5EcgAUMXwSNLKiccv3iQevwgAKA+cIQMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAANUq+gCKrv8/HxZluX4CE0AAK7m7u6ugICAG/YjkG8SzxcFAJQHHi4BAIABuIYMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhklMm/ePAUEBDi+wsLC9Pvf/16bN28ul/mXLVvm9DSUXbt2KSAgoNRP0Ro/frx69epVLjX9p7LUNG/ePO3du/eW1IOKc+V4eOKJJ4r87L//+7/VrVu3Cqiqcli2bFm5/btR1RDIKLHq1avrgw8+0AcffKBp06YpNzdXzz333C0JnLZt2+qDDz5Qy5Yty33usipLTfPnz9d33313C6tCRdq9e7d27dpV0WVUKitWrCCQi8HjF1FiLi4uCg4Odnx/zz336MEHH9Q//vEPdejQoVy3VatWLadtmcDEmlBxPD091apVK7355psKCwur6HKMl5OTo+rVq1d0GUbjDBll5uPjo3r16ik1NVWS9K9//UsTJkzQww8/rKCgIPXs2VNvvPGG8vLynMZlZ2frxRdfVPv27XXfffcpPj5eBQUFTn2utTycm5urGTNmqHPnzgoMDFTfvn21YcOGG9aZlJSkoUOHKjg4WCEhIRo1apSj5iuysrI0btw4tW/fXp06ddIbb7yhpUuX3nAZ/e9//7uioqIUFBTkWMbft2+fJDnGxsfHO5b6OZuqWoYPH66dO3ded5UoJSVFo0aNUkhIiIKDgzV06FAlJSU59enWrZumTp2q9957T127dlVISIiGDx+u8+fPl6iOTZs2acCAAQoKCtJ9992nV199VRcvXpQkXb58Wf3799fAgQOdjrO33npL7dq1088//yzpt2X49u3ba9++fXr00UcVGBioiIgIffPNN6XanvTvY2XTpk0aNWqUOnTooNGjR6tbt25KSUnRe++95zgmVq9eXaJ9vB1whowyu3DhgjIyMtSkSRNJUlpamurUqaMJEybIbrfr2LFjmjdvns6ePasZM2Y4xk2cOFHffvutxo0bpyZNmuh//ud/9Nlnn91we+PGjdO3336rF154QX5+fvr00081cuRILViwQA8//PA1x5w6dUpPPvmkmjZtqtdff125ubmaM2eOnnzySa1Zs0a1atWSJE2YMEE7d+7Un/70J91555368MMP9eOPP163nn/+8596+eWXNWTIED344IPKycnRvn37lJWVJUn64IMPNGjQIA0ePNhxXbtVq1Y3fmFRaXTt2lVt2rTRggULtGTJkiI/z87O1uDBg+Xi4qIpU6bIw8NDCxcudPz+NWrUyNH366+/1vHjxxUXF6e0tDTNmDFD06ZN05w5c65bQ0JCgmJjY9W/f3+NHDlSZ8+e1ezZs5WZmak5c+aoWrVqev3119WvXz8tWrRII0aM0M8//6y5c+dq1KhRuvvuux1z5efnKzY2VkOGDFGTJk20atUqPf/881q9erXjD8wbbe9qr7zyivr06aMFCxbIxcVFtWvX1rBhw9ShQwcNGTJEktSsWbMyv/5VjgWUwNy5c63g4GArPz/fys/Pt1JSUqwXXnjBuvfee60jR45cc0x+fr61Zs0aq02bNtbFixcty7KsQ4cOWQEBAdZHH33k6Hf58mWrW7dulr+/v6Nt586dlr+/v7Vv3z7LsizrwIEDlr+/v7Vq1SqnbQwaNMjq16+f4/uXXnrJioqKcnw/ffp0Kzg42EpLS3O0HT582AoICLBWrFjhqMnf39/65JNPHH0KCgqsnj17Xremd955xwoNDb3u6+bv72+988471+2DyufK8WBZlrV+/XrL39/f+uGHHyzLsqzXXnvN6tq1q2VZlrV8+XIrICDAOnz4sGNsWlqaFRwcbM2YMcPR1rVrV6tLly5Wbm6u0zbatm1rFRQUFFtHYWGh1bVrV2vMmDFO7Zs3b7YCAgKsgwcPOtr+9re/WW3btrX27Nlj9erVy/r973/vNPfcuXMtf3//ax6bsbGxpdrelWMlLi6uSM1du3a1pkyZUuw+3c5YskaJXbx4UW3btlXbtm3VtWtXrV+/XvHx8fLz85MkWZalZcuWKTIyUkFBQWrbtq3GjRuny5cv68SJE5KkxMREWZalHj16OOZ1dXVV9+7dr7vtPXv2SJJ+97vfObVHRETop59+clouu9ru3bsVFhamOnXqONpatmypu+++2zHnlSXoq8+yXVxc1LVr1+vW1KZNG6Wnp2v8+PHatm2bLl26dN3+qJp69Oghf39/LViwoMjPdu/erbvuusvpRsA6dero/vvvd/z+XXHvvffK3d3d8X3Lli2Vn5+vc+fOSZIKCgp0+fJlx5ckHT16VCkpKYqIiHD6WWhoqFxcXLR//37HfNHR0Wrfvr2io6N18uRJzZo1Sy4uRSPgWsfmDz/8UOrtSdJDDz1U0pcRYskapVC9enW9++67sixLx44d0+zZs/XSSy9p7dq1atiwoZYvX65Zs2bpmWeeUVhYmOx2uxITEzV16lTl5uZKks6ePSs3Nzd5eXk5zV2/fv3rbjsjI0Nubm5OwSpJDRo0kGVZysrKkqenZ5FxmZmZat26dZH2+vXrKyMjw6mm2rVrO/WpV6/edWvq1KmT4uPjtWLFCg0dOlQeHh565JFHNHHixCJ1ouqy2Wx67rnnNGbMmCKXOTIzM9WgQYMiY+rXr69Dhw45tdntdqfvr4TzlWOnR48eSklJcfx848aNSktLkySNGDHimrWdOnXKqc6oqCj97//+r7p166amTZsW6V/csXn27FlJKtX2roxFyRHIKDEXFxcFBgZKkoKCguTr66uBAwdqwYIFmjJlihISEtStWzeNHTvWMebIkSNOc3h7eys/P18ZGRlOB/6Vs4DieHl5XXPcr7/+KpvNViRMrx53rbnPnTunFi1aONWUlZXlNE9Jbqjp27ev+vbtq/Pnz2vjxo2aMWOGqlWrpunTp99wLKqOiIgIzZs3T2+++aYaN27saPfy8tLRo0eL9D937lyR4LuRhQsXOt0g2bBhQ0dYx8XFKSgoqMiYhg0bOv77zJkzmjNnjtq0aaP169drx44d6tSpk1P/4o5Nb29vSXL8oVmS7Um//RGAkmPJGmUWGBioqKgorV69WmfPnlVOTo7c3Nyc+qxdu7bIGElOd0cXFBToq6++uu62QkJCJP12Q8nVEhIS1KZNm2ueHV8Zt3PnTsfZsCQlJycrKSnJMWe7du0k/XbGcUVhYeE17y4tTr169TRgwACFh4crOTnZ0e7m5ub4RxNVl4uLi5577jlt3LjR6Q7qkJAQHTx40Ol3IiMjQ9u3b3f8/pVUQECAAgMDHV/u7u7y8/PTHXfcoRMnTjj97MqXj4+PY/zLL78sLy8vvffee3r44Yc1ceJEZWdnF9nOtY7Ne+65R5JKtb3icEwUjzNk3JThw4dr3bp1Wr58ue6//36tWLFC7777rlq0aKE1a9bo+PHjTv1btWqlHj16aPr06crNzXXcZZ2fn3/d7dx9993q2bOnZs6cqZycHPn6+mrNmjX67rvv9OabbxY77qmnntLq1as1ZMgQ/fGPf1Rubq7+8pe/qFGjRurXr58k6a677lKPHj302muv6dKlS2rcuLE+/PBD5eTkXPcv/Llz5yo9PV2hoaGqX7++Dh48qG+//VZPPfWUo4+fn582btyojh07qkaNGvL19XXc2Y2qpXfv3lqwYIF27dqlO++8U5LUv39/LVu2TDExMXrhhRccd1lXq1ZN0dHRN71Nm82m8ePHa9y4cbp48aIeeugh1ahRQ6mpqdq8ebNiY2Pl6+urVatWafv27Xr33Xfl6empqVOnqlevXnrttdc0c+ZMx3xubm5auHCh49hctWqVTp8+7bg+XtLtXY+fn5927typbdu2yW63q0mTJqpbt+5NvxZVAYGMm+Ln56fIyEitWrVKmzZtUlpamubOnStJeuSRRzRp0iQ999xzTmOmT5+uqVOn6s9//rPc3d3Vr18/hYaGKj4+/rrbev311/XGG2/o7bffVnp6uvz8/DR37tzrfkxho0aNtHLlSsXHx2vcuHFycXFReHi4xo8f7xSMV2qKj4931HTXXXfpvffeK3buwMBALV++XF988YWys7N1xx13aOjQofrjH//o6BMXF6fp06fr2WefVU5OjlasWMGHSFRRrq6uGjZsmCZNmuRoq1WrllauXKmZM2fqlVdeUWFhoTp06KB3333X6S1PNyMiIkJ2u12LFi1yrEjdeeedeuCBB9SgQQP98ssvio+P19ChQx0f4FO/fn1NmzZNI0aMUPfu3R03Vbq5uemNN97QlClTdPDgQTVp0kRz5851emvUjbZ3I2PGjNHkyZM1cuRIXbhwQTNmzFD//v3L5bWo7GyWZVkVXQRgoieeeEIuLi5auXJlRZcC3HLz5s3T0qVL+ajXCsQZMiBp/fr1OnXqlPz9/XXp0iV99tln2r179zXfygIAtwKBDOi3zyX+9NNPdezYMeXn58vPz0+vv/76Dd8fDQDlhSVrAAAMwNueAAAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAMrkykPrT548WdGlAFUC70MGKrnVq1drwoQJju9dXV1Vv359hYeHKzY2tkQf+A+g4hHIQBUxatQoNWnSRHl5efr+++/1ySefaM+ePfrss8/k4eFR7tvr27evoqKiHM/tBXBzCGSgiujSpYvj8ZYDBgxQ3bp19fbbb2vjxo2KjIws9+25urrK1dW13Oe9VS5duqQaNWpUdBlAsbiGDFRRHTt2lCSdOHHC0XbkyBGNGjVKoaGhCgwMVP/+/Z2eA52YmKiAgAB98sknReb79ttvFRAQ4HhOdHHXkDdv3qzHH39cwcHBat++vYYNG6ZDhw45fr5x40YFBATo559/drStX79eAQEBev75553mioiI0AsvvODU9umnn6p///4KCgpSaGioYmNjderUKac+gwcPVq9evbR//3498cQTuueee/TGG2+U5GUDKgyBDFRRKSkpkiS73S5JOnTokAYNGqQjR47o2Wef1fjx4+Xp6akRI0Y4HkofGBiopk2b6osvvigy37p16+Tl5aXOnTsXu81//OMfiomJkaenp8aNG6fhw4fr8OHDevzxxx3BHRISIpvNpt27dzvG7d69Wy4uLtqzZ4+j7fz580pOTta9997raFu4cKFeeuklNW/eXOPHj9cf/vAH7dixQ0888YQyMzOdaklPT9ezzz6r1q1ba+LEiTz2EuazAFRqH3/8seXv729t377dOnfunHXq1CkrISHBuu+++6x27dpZp06dsizLsqKjo61evXpZubm5jrGFhYXWoEGDrJ49ezraZs+ebbVt29ZKT093tOXm5lodO3a0JkyYUGS7J06csCzLsrKzs62OHTtakyZNcqrv7NmzVkhIiFN7VFSUNXr0aMf3/fr1s0aNGmX5+/tbhw8ftizLsr788kvL39/fOnDggGVZlnXy5EmrdevW1sKFC53mT0pKstq0aePU/uSTT1r+/v7WqlWrSvdiAhWIM2SginjqqafUqVMnPfjggxo1apRq1KihhQsX6o477lB6erp27typiIgIZWdn6/z58zp//rzS0tLUuXNnHTt2TGfOnJEkRUZGKj8/X19++aVj7m3btikzM/O616K3b9+uzMxMRUVFOeY/f/68XFxcdM8992jXrl2OviEhIY4z5OzsbP38888aNGiQ6tat6zhL3r17t+x2u/z9/SVJGzZsUGFhoSIiIpzmb9CggZo3b+40vyS5u7vz4HtUKtzUBVQRcXFx8vX1VVZWlj7++GP985//dNwB/csvv8iyLP31r3/VX//612uOP3funHx8fHT33XfLz89PX3zxhQYMGCDpt+XqunXr6r777it2+8eOHZMkRUdHX/PntWrVcvx3x44d9f777+v48eP65ZdfZLPZFBwcrI4dO2r37t0aOHCgdu/erQ4dOsjFxcUxv2VZ6tmz5zXnr1bN+Z8zHx8f7gBHpUIgA1VEUFCQ4y7r7t276/HHH9fYsWOVkJCgwsJCSdKQIUP0wAMPXHN8s2bNHP8dGRmpRYsW6fz586pVq5a+/vprRUVFFQm9q1n//0mu8fHx8vb2LvLzq+/IDgkJkST985//1IkTJ9SmTRt5enqqY8eOWrFihS5cuKADBw443dBVWFgom82mt99++5p3d3t6ejp9X7169WJrBUxEIANVkKurq8aMGaM//OEPeu+99/Rf//VfkiQ3Nzfdf//9NxwfGRmp+fPn68svv1SDBg2UnZ2tqKio645p2rSpJKl+/fo33Ebjxo3VuHFj7dmzRydOnHDcEd6xY0fNmDFDCQkJKigocLqhq1mzZrIsS02aNJGvr+8N9wGobLiGDFRRYWFhCgoK0vLly1WrVi2Fhobqgw8+0L/+9a8ifc+fP+/0fcuWLeXv769169Zp3bp18vb2dgrHa3nggQdUq1YtLV68WPn5+TfcRkhIiHbu3Kl9+/Y5zphbt26tmjVr6q233lL16tXVtm1bR/+ePXvK1dVV8+fPd5yNX2FZltLS0q7/ggCG4wwZqMKGDh2q0aNHa/Xq1Xr11Vf1+OOPq3fv3ho4cKCaNm2qX3/9Vd9//71Onz6tNWvWOI2NjIzU3Llz5eHhoUcffdRxLbc4tWrV0uTJk/Xiiy+qf//+ioyMVL169ZSamqrNmzerQ4cOiouLc/Tv2LGj1q5dK5vN5ghkV1dXtW/fXlu3blVoaKjTNeBmzZrphRde0OzZs5WSkqLu3burZs2aOnnypL766isNHDhQQ4cOLcdXD/i/RSADVVjPnj3VrFkzLV26VAMHDtTHH3+s+fPn65NPPlF6errq1aunNm3aaMSIEUXGRkZG6i9/+YsuXbqkiIiIEm2vd+/eatiwod566y0tWbJEeXl58vHxUceOHYvc8XxlmdrPz09169Z1at+6davj51cbNmyYWrRooWXLlmnBggWSpDvuuEPh4eHq1q1biV8XwEQ26z/XfgAAwP85riEDAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABvh/TQvUbB2MtXIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -416,7 +548,7 @@
     }
    ],
    "source": [
-    "g = sns.catplot(data=xdf, x=\"Reviewer\", y=\"dice\", kind=\"box\")\n",
+    "g = sns.catplot(data=xdf, x=\"Reviewer\", y=\"dice\", kind=\"box\", palette=revewer_cmap)\n",
     "g.set_axis_labels(\"Reviewer\", \"DSC\")"
    ]
   },
@@ -429,22 +561,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f91600e42b0>"
+       "<seaborn.axisgrid.FacetGrid at 0x7feac5d37730>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -454,8 +586,14 @@
     }
    ],
    "source": [
-    "g = sns.catplot(data=xdf, x=\"Reviewer\", y=\"mean_surface_distance_tol_7\", kind=\"box\")\n",
-    "g.set_axis_labels(\"Reviewer\", \"Normalized Surface Dice\\n(tolerance=7mm)\")\n"
+    "g = sns.catplot(\n",
+    "    data=xdf,\n",
+    "    x=\"Reviewer\",\n",
+    "    y=\"mean_surface_distance_tol_7\",\n",
+    "    kind=\"box\",\n",
+    "    palette=revewer_cmap,\n",
+    ")\n",
+    "g.set_axis_labels(\"Reviewer\", \"Normalized Surface Dice\\n(tolerance=7mm)\")"
    ]
   },
   {
@@ -467,22 +605,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f9160053eb0>"
+       "<seaborn.axisgrid.FacetGrid at 0x7feac5d2a590>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -492,8 +630,10 @@
     }
    ],
    "source": [
-    "g = sns.catplot(data=xdf, x=\"Reviewer\", y=\"hausdorff_distance_95\", kind=\"box\")\n",
-    "g.set_axis_labels(\"Reviewer\", \"95% Hausdorff Distance (mm)\")\n"
+    "g = sns.catplot(\n",
+    "    data=xdf, x=\"Reviewer\", y=\"hausdorff_distance_95\", kind=\"box\", palette=revewer_cmap\n",
+    ")\n",
+    "g.set_axis_labels(\"Reviewer\", \"95% Hausdorff Distance (mm)\")"
    ]
   },
   {
@@ -505,22 +645,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Radiologist: Kendall-τ: 0.86, p-value: 0.01\n",
+      "        Non-expert: Kendall-τ: 0.98, p-value: 0.00\n",
+      "        \n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "[Text(0.5, 19.049999999999997, 'Likert Score'), Text(27.375, 0.5, 'DSC')]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -530,16 +679,39 @@
     }
    ],
    "source": [
+    "x = rad_df[[\"LikertScore\", \"dice\"]].dropna()\n",
+    "rad_tau, rad_p_value = stats.kendalltau(x.LikertScore.values, x.dice.values)\n",
+    "x = ne_df[[\"LikertScore\", \"dice\"]].dropna()\n",
+    "ne_tau, ne_p_value = stats.kendalltau(x.LikertScore.values, x.dice.values)\n",
+    "\n",
+    "print(\n",
+    "    f\"\"\"\\\n",
+    "        Radiologist: Kendall-\\u03C4: {rad_tau:.2f}, p-value: {rad_p_value:.2f}\n",
+    "        Non-expert: Kendall-\\u03C4: {ne_tau:.2f}, p-value: {ne_p_value:.2f}\n",
+    "        \"\"\"\n",
+    ")\n",
+    "\n",
     "# plot likert score vs dice\n",
-    "g = sns.swarmplot(data=xdf, x=\"LikertScore\", y=\"dice\", hue=\"Reviewer\")\n",
-    "g.set(xlabel=\"Likert Score\", ylabel=\"DSC\")\n"
+    "g = sns.swarmplot(\n",
+    "    data=xdf, x=\"LikertScore\", y=\"dice\", hue=\"Reviewer\", palette=revewer_cmap\n",
+    ")\n",
+    "g.set(xlabel=\"Likert Score\", ylabel=\"DSC\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Radiologist: Kendall-τ: 0.86, p-value: 0.01\n",
+      "        Non-expert: Kendall-τ: 0.98, p-value: 0.00\n",
+      "        \n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -547,13 +719,13 @@
        " Text(27.375, 0.5, 'Normalized Surface Dice\\n(tolerance=7mm)')]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -563,7 +735,23 @@
     }
    ],
    "source": [
-    "# plot likert score vs dice\n",
+    "x = rad_df[[\"LikertScore\", \"mean_surface_distance_tol_7\"]].dropna()\n",
+    "rad_tau, rad_p_value = stats.kendalltau(\n",
+    "    x.LikertScore.values, x.mean_surface_distance_tol_7.values\n",
+    ")\n",
+    "x = ne_df[[\"LikertScore\", \"mean_surface_distance_tol_7\"]].dropna()\n",
+    "ne_tau, ne_p_value = stats.kendalltau(\n",
+    "    x.LikertScore.values, x.mean_surface_distance_tol_7.values\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"\"\"\\\n",
+    "        Radiologist: Kendall-\\u03C4: {rad_tau:.2f}, p-value: {rad_p_value:.2f}\n",
+    "        Non-expert: Kendall-\\u03C4: {ne_tau:.2f}, p-value: {ne_p_value:.2f}\n",
+    "        \"\"\"\n",
+    ")\n",
+    "\n",
+    "# plot likert score vs nsd\n",
     "g = sns.swarmplot(\n",
     "    data=xdf, x=\"LikertScore\", y=\"mean_surface_distance_tol_7\", hue=\"Reviewer\"\n",
     ")\n",
@@ -572,9 +760,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Radiologist: Kendall-τ: -0.86, p-value: 0.01\n",
+      "        Non-expert: Kendall-τ: -0.98, p-value: 0.00\n",
+      "        \n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -582,13 +779,13 @@
        " Text(41.875, 0.5, '95% Hausdorff Distance (mm)')]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -598,9 +795,25 @@
     }
    ],
    "source": [
+    "x = rad_df[[\"LikertScore\", \"hausdorff_distance_95\"]].dropna()\n",
+    "rad_tau, rad_p_value = stats.kendalltau(\n",
+    "    x.LikertScore.values, x.hausdorff_distance_95.values\n",
+    ")\n",
+    "x = ne_df[[\"LikertScore\", \"hausdorff_distance_95\"]].dropna()\n",
+    "ne_tau, ne_p_value = stats.kendalltau(\n",
+    "    x.LikertScore.values, x.hausdorff_distance_95.values\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"\"\"\\\n",
+    "        Radiologist: Kendall-\\u03C4: {rad_tau:.2f}, p-value: {rad_p_value:.2f}\n",
+    "        Non-expert: Kendall-\\u03C4: {ne_tau:.2f}, p-value: {ne_p_value:.2f}\n",
+    "        \"\"\"\n",
+    ")\n",
+    "\n",
     "# plot likert score vs dice\n",
     "g = sns.swarmplot(data=xdf, x=\"LikertScore\", y=\"hausdorff_distance_95\", hue=\"Reviewer\")\n",
-    "g.set(xlabel=\"Likert Score\", ylabel=\"95% Hausdorff Distance (mm)\")\n"
+    "g.set(xlabel=\"Likert Score\", ylabel=\"95% Hausdorff Distance (mm)\")"
    ]
   },
   {
@@ -613,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -642,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -655,7 +868,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -668,7 +881,7 @@
     "        \"mean_surface_distance\",\n",
     "        \"mean_surface_distance_tol_7\",\n",
     "    ]\n",
-    "].mean()\n"
+    "].mean()"
    ]
   },
   {

From 894b56e3257400820a7278cd8ab1780a331f0e65 Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Mon, 16 Oct 2023 19:04:12 +0000
Subject: [PATCH 2/2] update graphs

---
 .pre-commit-config.yaml   |   1 +
 model_performance.ipynb   | 199 ++++++++++++++++++++++++--------------
 qa-results/qa-results.csv | 196 ++++++++++++++++++-------------------
 3 files changed, 223 insertions(+), 173 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d019c73..78693f6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,6 +4,7 @@ repos:
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
+        exclude_types: [jupyter, csv]
       - id: check-yaml
       - id: check-merge-conflict
       - id: debug-statements
diff --git a/model_performance.ipynb b/model_performance.ipynb
index fd4b244..75e32e9 100644
--- a/model_performance.ipynb
+++ b/model_performance.ipynb
@@ -99,7 +99,7 @@
    "source": [
     "def download_inference(\n",
     "    extract_dir=\"qa-results\",\n",
-    "    url=\"https://zenodo.org/record/8400869/files/liver-ct.zip\",\n",
+    "    url=\"https://zenodo.org/record/10009368/files/liver-ct.zip\",\n",
     "):\n",
     "    # download the zip file and extract it\n",
     "    r = requests.get(url)\n",
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -238,22 +238,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7feac5b4d390>"
+       "<seaborn.axisgrid.FacetGrid at 0x7ff4034962c0>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640.75x500 with 1 Axes>"
       ]
@@ -263,32 +263,32 @@
     }
    ],
    "source": [
-    "df2 = val_df.groupby([\"Reviewer\", \"LikertScore\"]).count()\n",
-    "for reviewer in val_df[\"Reviewer\"].unique():\n",
-    "    for score in range(1, 6):\n",
-    "        if (reviewer, score) not in df2.index:\n",
-    "            df2.loc[(reviewer, score), \"SeriesInstanceUID\"] = 0\n",
-    "\n",
-    "df2.reset_index(inplace=True)\n",
-    "\n",
     "g = sns.catplot(\n",
-    "    data=df2,\n",
-    "    kind=\"bar\",\n",
+    "    data=val_df,\n",
+    "    kind=\"count\",\n",
     "    x=\"LikertScore\",\n",
-    "    y=\"SeriesInstanceUID\",\n",
+    "    order=[1, 2, 3, 4, 5],\n",
     "    hue=\"Reviewer\",\n",
-    "    orient=\"v\",\n",
+    "    hue_order=[\"Radiologist\", \"Non-expert\"],\n",
     "    palette=revewer_cmap,\n",
     ")\n",
-    "# g.despine(left=True)\n",
-    "g.set_axis_labels(\"Likert Score\", \"Counts\")\n",
-    "g.legend.set_title(\"\")\n",
-    "g.set(title=f\"Likert Score by Reviewer\")"
+    "g.set_axis_labels(\"\", \"Counts\")\n",
+    "g.set(title=f\"Likert Score by Reviewer\\nKendall's \\u03c4: {tau:.2f}\")\n",
+    "g.set_xticklabels(\n",
+    "    labels=[\n",
+    "        \"Strongly\\nDisagree\",\n",
+    "        \"Disagree\",\n",
+    "        \"Neither Agree\\nnor Disagree\",\n",
+    "        \"Agree\",\n",
+    "        \"Strongly\\nAgree\",\n",
+    "    ],\n",
+    "    rotation=60,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +297,7 @@
        "Text(0.5, 1.0, 'Likert Score Distribution for Radiologist')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -445,59 +445,98 @@
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
-       "    .dataframe thead th {\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">dice</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">hausdorff_distance_95</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance_tol_7</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
        "      <th></th>\n",
-       "      <th>dice</th>\n",
-       "      <th>hausdorff_distance_95</th>\n",
-       "      <th>mean_surface_distance</th>\n",
-       "      <th>mean_surface_distance_tol_7</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>label</th>\n",
        "      <th>Reviewer</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">liver</th>\n",
        "      <th>Non-expert</th>\n",
-       "      <td>0.970476</td>\n",
-       "      <td>5.574182</td>\n",
-       "      <td>0.869856</td>\n",
-       "      <td>0.951877</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>5.57</td>\n",
+       "      <td>12.71</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>2.08</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Radiologist</th>\n",
-       "      <td>0.970285</td>\n",
-       "      <td>5.662749</td>\n",
-       "      <td>0.814795</td>\n",
-       "      <td>0.956100</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>5.66</td>\n",
+       "      <td>10.75</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>1.67</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 dice  hausdorff_distance_95  mean_surface_distance  \\\n",
-       "Reviewer                                                              \n",
-       "Non-expert   0.970476               5.574182               0.869856   \n",
-       "Radiologist  0.970285               5.662749               0.814795   \n",
+       "                   dice       hausdorff_distance_95         \\\n",
+       "                   mean   std                  mean    std   \n",
+       "label Reviewer                                               \n",
+       "liver Non-expert   0.97  0.08                  5.57  12.71   \n",
+       "      Radiologist  0.97  0.07                  5.66  10.75   \n",
+       "\n",
+       "                  mean_surface_distance       mean_surface_distance_tol_7  \\\n",
+       "                                   mean   std                        mean   \n",
+       "label Reviewer                                                              \n",
+       "liver Non-expert                   0.87  2.08                        0.95   \n",
+       "      Radiologist                  0.81  1.67                        0.96   \n",
        "\n",
-       "             mean_surface_distance_tol_7  \n",
-       "Reviewer                                  \n",
-       "Non-expert                      0.951877  \n",
-       "Radiologist                     0.956100  "
+       "                         \n",
+       "                    std  \n",
+       "label Reviewer           \n",
+       "liver Non-expert   0.11  \n",
+       "      Radiologist  0.09  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -505,13 +544,23 @@
    "source": [
     "xdf[\n",
     "    [\n",
+    "        \"label\",\n",
     "        \"Reviewer\",\n",
     "        \"dice\",\n",
     "        \"hausdorff_distance_95\",\n",
     "        \"mean_surface_distance\",\n",
     "        \"mean_surface_distance_tol_7\",\n",
     "    ]\n",
-    "].replace([np.inf, -np.inf], np.nan).dropna().groupby(\"Reviewer\").mean()"
+    "].replace([np.inf, -np.inf], np.nan).dropna().groupby([\"label\", \"Reviewer\"]).agg(\n",
+    "    {\n",
+    "        \"dice\": [\"mean\", \"std\"],\n",
+    "        \"hausdorff_distance_95\": [\"mean\", \"std\"],\n",
+    "        \"mean_surface_distance\": [\"mean\", \"std\"],\n",
+    "        \"mean_surface_distance_tol_7\": [\"mean\", \"std\"],\n",
+    "    }\n",
+    ").round(\n",
+    "    2\n",
+    ")"
    ]
   },
   {
@@ -523,16 +572,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7feac5d371c0>"
+       "<seaborn.axisgrid.FacetGrid at 0x7ff280cdb610>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -561,16 +610,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7feac5d37730>"
+       "<seaborn.axisgrid.FacetGrid at 0x7ff280cda620>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -605,16 +654,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7feac5d2a590>"
+       "<seaborn.axisgrid.FacetGrid at 0x7ff280df8e50>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -645,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -663,7 +712,7 @@
        "[Text(0.5, 19.049999999999997, 'Likert Score'), Text(27.375, 0.5, 'DSC')]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -700,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -719,7 +768,7 @@
        " Text(27.375, 0.5, 'Normalized Surface Dice\\n(tolerance=7mm)')]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -760,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -779,7 +828,7 @@
        " Text(41.875, 0.5, '95% Hausdorff Distance (mm)')]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -826,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -855,20 +904,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dice                            0.944679\n",
-       "hausdorff_distance_95          18.005701\n",
-       "mean_surface_distance           2.195581\n",
-       "mean_surface_distance_tol_7     0.947793\n",
+       "dice                            0.94\n",
+       "hausdorff_distance_95          18.01\n",
+       "mean_surface_distance           2.20\n",
+       "mean_surface_distance_tol_7     0.95\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -881,7 +930,7 @@
     "        \"mean_surface_distance\",\n",
     "        \"mean_surface_distance_tol_7\",\n",
     "    ]\n",
-    "].mean()"
+    "].mean().round(2)"
    ]
   },
   {
diff --git a/qa-results/qa-results.csv b/qa-results/qa-results.csv
index fb93557..d13a805 100644
--- a/qa-results/qa-results.csv
+++ b/qa-results/qa-results.csv
@@ -1,99 +1,99 @@
-Reviewer,Validation,Collection,PatientID,StudyDate,StudyDate_suffix,StudyInstanceUID,SeriesInstanceUID,AISegmentation,LikertScore,CommentsAboutAISegmentation,CommentsAboutScan,CorrectedSegmentation
-rad1,TRUE,tcga-lihc,TCGA-DD-A1EH,20010629,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.150731818010634307938925068434,ai_TCGA-DD-A1EH_20010629_1.seg.dcm,5,no corrections to segmentation,,
-rad1,TRUE,tcga-lihc,TCGA-DD-A1EK,20010531,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.145559228873850789419206611274,1.3.6.1.4.1.14519.5.2.1.3344.4008.141679692047619086484827875122,ai_TCGA-DD-A1EK_20010531_0.seg.dcm,4,,,rad1_TCGA-DD-A1EK_20010531_0.seg.dcm
-rad1,TRUE,tcga-lihc,TCGA-DD-A1EL,20020116,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.158425276417455187125782049563,1.3.6.1.4.1.14519.5.2.1.3344.4008.269121785256966585574860555086,ai_TCGA-DD-A1EL_20020116_0.seg.dcm,4,,,rad1_TCGA-DD-A1EL_20020116_0.seg.dcm
-rad1,TRUE,tcga-lihc,TCGA-DD-A4NK,19980326,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.153767944691753512045004643297,1.3.6.1.4.1.14519.5.2.1.3344.4008.298723806090585681738761928284,ai_TCGA-DD-A4NK_19980326_0.seg.dcm,5,no change in segmentation,,
-rad1,TRUE,tcga-lihc,TCGA-DD-A4NL,20000323,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.123435557800692096834678469001,1.3.6.1.4.1.14519.5.2.1.3344.4008.295183335434779171346930899761,ai_TCGA-DD-A4NL_20000323_0.seg.dcm,5,No change to segmentation,,
-rad1,TRUE,tcga-lihc,TCGA-DD-A4NS,20001011,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.283574261555925310865604827346,ai_TCGA-DD-A4NS_20001011_2.seg.dcm,4,few edits,,rad1_TCGA-DD-A4NS_20001011_2.seg.dcm
-rad1,TRUE,tcga-lihc,TCGA-DD-A4NV,20050902,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.989114914849856678473558553333,1.3.6.1.4.1.14519.5.2.1.3344.4008.288401067690076379005498612621,ai_TCGA-DD-A4NV_20050902_0.seg.dcm,5,no changes to segmentation,,
-rad1,TRUE,tcga-lihc,TCGA-G3-A5SK,20050701,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.291273592266846387761210025624,1.3.6.1.4.1.14519.5.2.1.1079.4008.318266519014308109379919043732,ai_TCGA-G3-A5SK_20050701_0.seg.dcm,3,,,rad1_TCGA-G3-A5SK_20050701_0.seg.dcm
-rad1,TRUE,tcga-lihc,TCGA-G3-A6UC,20060610,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.273569008619736834081366735502,1.3.6.1.4.1.14519.5.2.1.1079.4008.972951195130883284724850114185,ai_TCGA-G3-A6UC_20060610_0.seg.dcm,5,no change to segmentation,,
-ne1,TRUE,tcga-lihc,TCGA-DD-A1EH,20010629,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.150731818010634307938925068434,ai_TCGA-DD-A1EH_20010629_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,TRUE,tcga-lihc,TCGA-DD-A1EK,20010531,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.145559228873850789419206611274,1.3.6.1.4.1.14519.5.2.1.3344.4008.141679692047619086484827875122,ai_TCGA-DD-A1EK_20010531_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,TRUE,tcga-lihc,TCGA-DD-A1EL,20020116,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.158425276417455187125782049563,1.3.6.1.4.1.14519.5.2.1.3344.4008.269121785256966585574860555086,ai_TCGA-DD-A1EL_20020116_0.seg.dcm,3,Did not fully segment liver in first few slices. Needed adjustment.,,ne1_TCGA-DD-A1EL_20020116_0.seg.dcm
-ne1,TRUE,tcga-lihc,TCGA-DD-A4NK,19980326,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.153767944691753512045004643297,1.3.6.1.4.1.14519.5.2.1.3344.4008.298723806090585681738761928284,ai_TCGA-DD-A4NK_19980326_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,TRUE,tcga-lihc,TCGA-DD-A4NL,20000323,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.123435557800692096834678469001,1.3.6.1.4.1.14519.5.2.1.3344.4008.295183335434779171346930899761,ai_TCGA-DD-A4NL_20000323_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,TRUE,tcga-lihc,TCGA-DD-A4NS,20001011,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.283574261555925310865604827346,ai_TCGA-DD-A4NS_20001011_2.seg.dcm,2,AI did not segment the entire liver in the majority of slices where it was present. Adjustment needed.,,ne1_TCGA-DD-A4NS_20001011_2.seg.dcm
-ne1,TRUE,tcga-lihc,TCGA-DD-A4NV,20050902,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.989114914849856678473558553333,1.3.6.1.4.1.14519.5.2.1.3344.4008.288401067690076379005498612621,ai_TCGA-DD-A4NV_20050902_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,TRUE,tcga-lihc,TCGA-G3-A5SK,20050701,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.291273592266846387761210025624,1.3.6.1.4.1.14519.5.2.1.1079.4008.318266519014308109379919043732,ai_TCGA-G3-A5SK_20050701_0.seg.dcm,3,"Many slices needed adjustment as either the entire liver was not included or far too much area was included. AI was able, however, to segment the majority of the liver in each slice.",,ne1_TCGA-G3-A5SK_20050701_0.seg.dcm
-ne1,TRUE,tcga-lihc,TCGA-G3-A6UC,20060610,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.273569008619736834081366735502,1.3.6.1.4.1.14519.5.2.1.1079.4008.972951195130883284724850114185,ai_TCGA-G3-A6UC_20060610_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-BC-A10Z,19931220,0,1.3.6.1.4.1.14519.5.2.1.8421.4008.338750756821159002165351451130,1.3.6.1.4.1.14519.5.2.1.8421.4008.761093011533106086639756339870,ai_TCGA-BC-A10Z_19931220_0.seg.dcm,5,Accurate segmentation of liver. No adjustment necessary.,,
-ne1,FALSE,tcga-lihc,TCGA-BC-A3KF,20020218,0,1.3.6.1.4.1.14519.5.2.1.8421.4008.212149247533463904570758018240,1.3.6.1.4.1.14519.5.2.1.8421.4008.172664185005829912144086258851,ai_TCGA-BC-A3KF_20020218_0.seg.dcm,4,Adequate segmentation of liver. Some overextension of segmentation region in few of the top slides.,,
-ne1,FALSE,tcga-lihc,TCGA-BC-A69H,20030527,1,1.3.6.1.4.1.14519.5.2.1.8421.4008.232432715274855073157517341855,1.3.6.1.4.1.14519.5.2.1.8421.4008.220393017361571445089925754168,ai_TCGA-BC-A69H_20030527_1.seg.dcm,5,Accurate segmentation of liver. No adjustment necessary.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A115,19960329,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.273311785136119701888483417354,1.3.6.1.4.1.14519.5.2.1.3344.4008.334650040258165055970898598044,ai_TCGA-DD-A115_19960329_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A115,19960512,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.111190381797398190611685670115,1.3.6.1.4.1.14519.5.2.1.3344.4008.161980713684483394055711266086,ai_TCGA-DD-A115_19960512_0.seg.dcm,4,"Liver adequately segmented. Shape of segmentation area in top slides is not perfect, but adjustment is not super necessary.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A116,19960314,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.379064229494090410137584103828,1.3.6.1.4.1.14519.5.2.1.3344.4008.135494304833236144987187674002,ai_TCGA-DD-A116_19960314_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A11A,19970706,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.561613401719690898408844572706,1.3.6.1.4.1.14519.5.2.1.3344.4008.391094057748793176042775715108,ai_TCGA-DD-A11A_19970706_0.seg.dcm,2,"Over-segmentation of region directly adjacent to liver in the majority of slices where the liver is present. Segmentation needs correction. In only a handful of the bottom slices, the liver is segmented adequately.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A11C,19981028,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.229699852250561373965265745922,1.3.6.1.4.1.14519.5.2.1.3344.4008.114015423829654026617391168242,ai_TCGA-DD-A11C_19981028_2.seg.dcm,4,Liver is adequately segmented. The segmentation area can be increased outwards in the last few slices on the top.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A11D,20000913,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.213393886593375153276050327721,1.3.6.1.4.1.14519.5.2.1.3344.4008.312604581201752148340133119341,ai_TCGA-DD-A11D_20000913_0.seg.dcm,4,"Liver is adequately segmented. A few of the middle slices do not cover the entirety of the liver, but it is a very small portion.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1E9,19950906,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.824746819228131664143570751388,1.3.6.1.4.1.14519.5.2.1.3344.4008.208949456379783541277979256617,ai_TCGA-DD-A1E9_19950906_0.seg.dcm,3,"For the most part, the liver is adequately segmented. There are a few slices where it can be adjusted. The spleen also appears to be segmented in numerous slices.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EH,20010227,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.114918398611791628520770797703,1.3.6.1.4.1.14519.5.2.1.3344.4008.528527982325292531978902370574,ai_TCGA-DD-A1EH_20010227_0.seg.dcm,4,Adequate segmentation of liver. Some overextension of segmentation region in few of the middle slices.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EK,20010609,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.103259826517251415276965927375,1.3.6.1.4.1.14519.5.2.1.3344.4008.162751390639576496099577915901,ai_TCGA-DD-A1EK_20010609_0.seg.dcm,3,"AI was able to identify and segment liver, but there are many slices with gaps inside the liver region that need adjustment.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EL,20020605,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.143224773631509814038880620336,1.3.6.1.4.1.14519.5.2.1.3344.4008.265501579320933065719451395150,ai_TCGA-DD-A1EL_20020605_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NA,20020110,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.160220435632848389630559335170,1.3.6.1.4.1.14519.5.2.1.3344.4008.374038589141774665244999247652,ai_TCGA-DD-A4NA_20020110_1.seg.dcm,4,Adequate segmentation of liver. Some overextension of segmentation region in few of the top slices.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4ND,19980101,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.663582373061408434045464445433,1.3.6.1.4.1.14519.5.2.1.3344.4008.122333400409451122879233480510,ai_TCGA-DD-A4ND_19980101_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4ND,19980103,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.141621615283764837404351584644,1.3.6.1.4.1.14519.5.2.1.3344.4008.240151200781717301848509985012,ai_TCGA-DD-A4ND_19980103_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4ND,19991229,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.294765947948814153890689694139,1.3.6.1.4.1.14519.5.2.1.3344.4008.144208613011399899455044558161,ai_TCGA-DD-A4ND_19991229_0.seg.dcm,4,Liver adequately segmented. There are some random segmentation voxels in the center of the image in a few of the top slices (irrespective to the liver).,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NE,20030830,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.217069790726612801841206031642,1.3.6.1.4.1.14519.5.2.1.3344.4008.309006815412086883143504283501,ai_TCGA-DD-A4NE_20030830_1.seg.dcm,2,"Coronal and sagittal image quality not great. In a few slices, AI segmented irrelevant organs or did not fully cover the entire liver.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NE,20040319,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.149922237131000847016153692128,1.3.6.1.4.1.14519.5.2.1.3344.4008.882557897404715844683325639915,ai_TCGA-DD-A4NE_20040319_0.seg.dcm,5,,Liver accurately segmented. No adjustment necessary,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NG,20020517,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.233811296314642551471614789062,1.3.6.1.4.1.14519.5.2.1.3344.4008.313796055651915079436969192808,ai_TCGA-DD-A4NG_20020517_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NG,20030911,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.277011436751126552397576927159,1.3.6.1.4.1.14519.5.2.1.3344.4008.460919767301516578653712060606,ai_TCGA-DD-A4NG_20030911_1.seg.dcm,4,Liver adequately segmented. There are a few slices where the segmentation area can be increased slightly.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NI,20020216,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.302612097544963316088092090676,1.3.6.1.4.1.14519.5.2.1.3344.4008.224343227507905277467666606717,ai_TCGA-DD-A4NI_20020216_0.seg.dcm,5,Liver accurately segmented. Little to no adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NI,20040521,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.175826608307498311132229109398,1.3.6.1.4.1.14519.5.2.1.3344.4008.287212806515445440668495171006,ai_TCGA-DD-A4NI_20040521_1.seg.dcm,3,Liver for the most part adequately segmented. There are a few slices where the bottom of the liver are not included in the segmentation (this is most visible in the coronal view) or areas of the liver in general are not included.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20040227,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.177316748388312779271301192905,1.3.6.1.4.1.14519.5.2.1.3344.4008.115264785656904542056592486066,ai_TCGA-DD-A4NJ_20040227_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20050324,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.178571847081305757756952090882,1.3.6.1.4.1.14519.5.2.1.3344.4008.571653965400772551680793228390,ai_TCGA-DD-A4NJ_20050324_1.seg.dcm,4,The liver is adequately segmented. There are a few slices where the segmentation can be extended outwards slightly.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20051015,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.151511465574060586762887334921,1.3.6.1.4.1.14519.5.2.1.3344.4008.476340434793464019964427888823,ai_TCGA-DD-A4NJ_20051015_1.seg.dcm,4,The liver is adequately segmented. There are a few slices where the segmentation can increase outwards slightly.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20000707,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.283305018370962482092363755149,1.3.6.1.4.1.14519.5.2.1.3344.4008.698509830523414701636495235205,ai_TCGA-DD-A4NL_20000707_2.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010104,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.295852698560353190326329207249,1.3.6.1.4.1.14519.5.2.1.3344.4008.965531043212955882044413761437,ai_TCGA-DD-A4NL_20010104_2.seg.dcm,3,The liver is for the most part segmented adequately. There are random segmentation voxels in irrelevant organs or in the breast tissue in the first few slices. There are also a few slices where the entire liver is not fully included in the segmentation.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010711,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.217381303091455644784847397746,1.3.6.1.4.1.14519.5.2.1.3344.4008.252117527833266466109320709457,ai_TCGA-DD-A4NL_20010711_2.seg.dcm,2,Liver not fully segmented in many slices. Large portions of liver excluded.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20011211,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.924229943132737214631304598948,1.3.6.1.4.1.14519.5.2.1.3344.4008.109254970283977303397073728772,ai_TCGA-DD-A4NL_20011211_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NN,19970122,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.334960828217735997056926407680,1.3.6.1.4.1.14519.5.2.1.3344.4008.398727212485699636033896987425,ai_TCGA-DD-A4NN_19970122_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NN,19970709,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.238875024531656123348758022775,1.3.6.1.4.1.14519.5.2.1.3344.4008.207848934776528286087485176422,ai_TCGA-DD-A4NN_19970709_0.seg.dcm,4,"The liver is adequately segmented. In a few of the top slices, the segmentation can increase outwards slightly.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20050525,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.226816019260191889484839567314,1.3.6.1.4.1.14519.5.2.1.3344.4008.508067462958962930439058636497,ai_TCGA-DD-A4NO_20050525_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20060103,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.759986268611549722078244299409,1.3.6.1.4.1.14519.5.2.1.3344.4008.218404660389610360651678437867,ai_TCGA-DD-A4NO_20060103_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NQ,20000129,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.194045555077154079746429047642,1.3.6.1.4.1.14519.5.2.1.3344.4008.224532092086806107041379081405,ai_TCGA-DD-A4NQ_20000129_0.seg.dcm,4,"Liver adequately segmented. In the first few bottom slices, there are some irrelevant extraneous segmentation voxels in other organs.",,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25S,20020915,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.117265092232754988757648971195,1.3.6.1.4.1.14519.5.2.1.1079.4008.186532305717125520774895527018,ai_TCGA-G3-A25S_20020915_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25U,20010216,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.405829857382526369105550547365,1.3.6.1.4.1.14519.5.2.1.1079.4008.142033825293496813496372398694,ai_TCGA-G3-A25U_20010216_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20000428,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.230977793203918304163491543099,1.3.6.1.4.1.14519.5.2.1.1079.4008.161656825189547046825817060109,ai_TCGA-G3-A25Y_20000428_1.seg.dcm,3,"Liver adequately segmented in the majority of slices. In the first few bottom slices, the entire liver area is not included in the segmentation.",,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20070617,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.207384642619260022321262977167,1.3.6.1.4.1.14519.5.2.1.1079.4008.747511138638455991207658889563,ai_TCGA-G3-A25Y_20070617_1.seg.dcm,3,"Liver is adequately segmented in the majority of slices. In a few of the top slices, the entire area of the liver is not included.",,
-ne1,FALSE,tcga-lihc,TCGA-G3-A3CH,20040507,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.116712770482771075241626231125,1.3.6.1.4.1.14519.5.2.1.1079.4008.275816260983984341641329560754,ai_TCGA-G3-A3CH_20040507_1.seg.dcm,4,"Liver adequately segmented. In some of the bottom slices, the entire liver is not included but this is a very small portion.",,
-ne1,FALSE,tcga-lihc,TCGA-G3-A3CI,20040715,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.324693330665701767725603699315,1.3.6.1.4.1.14519.5.2.1.1079.4008.118291050558345675291995888259,ai_TCGA-G3-A3CI_20040715_0.seg.dcm,2,"Irrelevant segmentation voxels in bottom slide. Liver identified by AI, but not fully segmented in many slides.",,
-ne1,FALSE,tcga-lihc,TCGA-G3-AAUZ,20051108,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.245846761312718553858837754385,1.3.6.1.4.1.14519.5.2.1.1079.4008.114832936145958292789896133590,ai_TCGA-G3-AAUZ_20051108_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-G3-AAV4,20060509,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377009651524040310426,1.3.6.1.4.1.14519.5.2.1.1079.4008.153956053674628255368772115120,ai_TCGA-G3-AAV4_20060509_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
-ne1,FALSE,tcga-lihc,TCGA-G3-AAV5,20070617,2,1.3.6.1.4.1.14519.5.2.1.1079.4008.328820149586454940911187796566,1.3.6.1.4.1.14519.5.2.1.1079.4008.161686441012277956341337038260,ai_TCGA-G3-AAV5_20070617_2.seg.dcm,1,"Irrelevant organs segmented. In the majority of the slices where it is present, the entire area of the liver is not included. Needs major adjustment.",,
-ne1,FALSE,tcga-lihc,TCGA-K7-A5RG,20000329,1,1.3.6.1.4.1.14519.5.2.1.3983.4008.335698561314795862034766172231,1.3.6.1.4.1.14519.5.2.1.3983.4008.224387668563746005693412217402,ai_TCGA-K7-A5RG_20000329_1.seg.dcm,4,"The liver is adequately segmented. In some of the top slices, the segmentation area can be increased outwards slightly.",,
-ne1,FALSE,tcga-lihc,TCGA-BC-A69H,20030527,0,1.3.6.1.4.1.14519.5.2.1.8421.4008.232432715274855073157517341855,1.3.6.1.4.1.14519.5.2.1.8421.4008.128712245047293209552994910188,ai_TCGA-BC-A69H_20030527_0.seg.dcm,4,"Adequate segmentation of liver. In a few of the top slices, the entire liver area is not included (segmentation area can be increased outwards slightly).",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A11C,19981028,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.229699852250561373965265745922,1.3.6.1.4.1.14519.5.2.1.3344.4008.163004960475005676802829596817,ai_TCGA-DD-A11C_19981028_0.seg.dcm,2,AI misses large portion of liver in many of the top slices.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A11C,19981028,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.229699852250561373965265745922,1.3.6.1.4.1.14519.5.2.1.3344.4008.115602691812578451725224669740,ai_TCGA-DD-A11C_19981028_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EH,20010629,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.336451395223174621319606562381,ai_TCGA-DD-A1EH_20010629_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EH,20010629,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.121901765028698957794203464452,ai_TCGA-DD-A1EH_20010629_2.seg.dcm,3,"Liver is adequately segmented in most slices. In the first few bottom slices where the liver is present, the segmentation region is awkwardly cut off towards the bottom left region of liver.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EL,20020116,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.158425276417455187125782049563,1.3.6.1.4.1.14519.5.2.1.3344.4008.153373588899215893192917369310,ai_TCGA-DD-A1EL_20020116_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A1EL,20020605,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.143224773631509814038880620336,1.3.6.1.4.1.14519.5.2.1.3344.4008.137417991108008645521008541818,ai_TCGA-DD-A1EL_20020605_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NA,20020110,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.160220435632848389630559335170,1.3.6.1.4.1.14519.5.2.1.3344.4008.172222876443325136473137847444,ai_TCGA-DD-A4NA_20020110_0.seg.dcm,4,"AI adequately segmented liver in almost all slices. In a few of the top slices, the AI left out small portions of the liver. ",Image quality is somewhat grainy.,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NE,20030830,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.217069790726612801841206031642,1.3.6.1.4.1.14519.5.2.1.3344.4008.504589444775630863861688726200,ai_TCGA-DD-A4NE_20030830_0.seg.dcm,4,"Liver adequately segmented. In a few of the bottom slices, the entire area of the liver is not included. Segmentation can be extended outwards slightly.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NG,20030911,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.277011436751126552397576927159,1.3.6.1.4.1.14519.5.2.1.3344.4008.136119724780535778270649472484,ai_TCGA-DD-A4NG_20030911_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NI,20040521,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.175826608307498311132229109398,1.3.6.1.4.1.14519.5.2.1.3344.4008.230419527952461164804294066822,ai_TCGA-DD-A4NI_20040521_0.seg.dcm,3,Liver is adequately segmented in a majority of slices. AI appears to segment half of a portal vein in the first few bottom slices.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20050324,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.178571847081305757756952090882,1.3.6.1.4.1.14519.5.2.1.3344.4008.127004125990655063673320541498,ai_TCGA-DD-A4NJ_20050324_0.seg.dcm,4,"Adequate segmentation of liver. In a few of the bottom slices, the top portion of the liver (from the axial view) is not included.",,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20051015,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.151511465574060586762887334921,1.3.6.1.4.1.14519.5.2.1.3344.4008.184445494707565935416050657382,ai_TCGA-DD-A4NJ_20051015_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20000707,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.283305018370962482092363755149,1.3.6.1.4.1.14519.5.2.1.3344.4008.141557981276332362184646880847,ai_TCGA-DD-A4NL_20000707_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20000707,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.283305018370962482092363755149,1.3.6.1.4.1.14519.5.2.1.3344.4008.223720200659092258881103564649,ai_TCGA-DD-A4NL_20000707_1.seg.dcm,2,AI missed large portions of the liver in many slices. Also included a portion of irrelevant muscle in a few of the bottom slices.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010104,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.295852698560353190326329207249,1.3.6.1.4.1.14519.5.2.1.3344.4008.174933394548192471772029827767,ai_TCGA-DD-A4NL_20010104_0.seg.dcm,2,AI missed large portions of the liver in many slices. Irrelevant segmentation of right kidney and portion of chest muscle.,Image quality is grainy.,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010104,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.295852698560353190326329207249,1.3.6.1.4.1.14519.5.2.1.3344.4008.217630104813016126487750234080,ai_TCGA-DD-A4NL_20010104_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010711,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.217381303091455644784847397746,1.3.6.1.4.1.14519.5.2.1.3344.4008.477051078981473110616142832395,ai_TCGA-DD-A4NL_20010711_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010711,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.217381303091455644784847397746,1.3.6.1.4.1.14519.5.2.1.3344.4008.101836454235477604240412504215,ai_TCGA-DD-A4NL_20010711_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20011211,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.924229943132737214631304598948,1.3.6.1.4.1.14519.5.2.1.3344.4008.177491661724537296017407228479,ai_TCGA-DD-A4NL_20011211_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NN,19970122,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.334960828217735997056926407680,1.3.6.1.4.1.14519.5.2.1.3344.4008.461026143927990958589293196591,ai_TCGA-DD-A4NN_19970122_0.seg.dcm,4,"Adequate segmentation of liver in all slices. In the first two to three bottom slices where the liver first emerges, the entire liver volume is not included.",Somewhat grainy image quality for coronal and sagittal slices.,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20050525,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.226816019260191889484839567314,1.3.6.1.4.1.14519.5.2.1.3344.4008.199400007458737404101255479212,ai_TCGA-DD-A4NO_20050525_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20060103,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.759986268611549722078244299409,1.3.6.1.4.1.14519.5.2.1.3344.4008.479401360926084327281964656390,ai_TCGA-DD-A4NO_20060103_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NS,20001011,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.200842643214043585407342107350,ai_TCGA-DD-A4NS_20001011_0.seg.dcm,2,AI missed large portions of liver in many slices. Also appears to segment spleen in some slices as well.,Image quality is grainy,
-ne1,FALSE,tcga-lihc,TCGA-DD-A4NS,20001011,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.206559987112338396022163503109,ai_TCGA-DD-A4NS_20001011_1.seg.dcm,3,"Adequate segmentation of liver in majority of slices. In some, the outer edge of the liver segmentation is not perfect, but it suffices. Irrelevant segmentation of spleen in some slices.",,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25U,20010216,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.405829857382526369105550547365,1.3.6.1.4.1.14519.5.2.1.1079.4008.108655443343069434585549901126,ai_TCGA-G3-A25U_20010216_0.seg.dcm,2,Large portion of liver left out of segmentation in many of the bottom slices. Segmentation is adequate as it goes to the top slices.,Grainy image quality.,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20000428,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.230977793203918304163491543099,1.3.6.1.4.1.14519.5.2.1.1079.4008.218353931327309701484061486682,ai_TCGA-G3-A25Y_20000428_0.seg.dcm,1,No segmentation of liver.,Coronal and sagittal images cut off when uploading to 3D slicer.,
-ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20070617,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.207384642619260022321262977167,1.3.6.1.4.1.14519.5.2.1.1079.4008.197655373334398563370393733433,ai_TCGA-G3-A25Y_20070617_0.seg.dcm,4,Liver adequately segmented in the majority of slices. There are some slices where the segmentation region can be extended outwards slightly.,,
-ne1,FALSE,tcga-lihc,TCGA-G3-A3CH,20040507,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.116712770482771075241626231125,1.3.6.1.4.1.14519.5.2.1.1079.4008.465478383053845803437953385013,ai_TCGA-G3-A3CH_20040507_0.seg.dcm,2,Irrelevant segmentation of muscle. AI did not accurately segment the liver.,,
-ne1,FALSE,tcga-lihc,TCGA-G3-A5SK,20050701,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.291273592266846387761210025624,1.3.6.1.4.1.14519.5.2.1.1079.4008.138620238014948298034813730954,ai_TCGA-G3-A5SK_20050701_1.seg.dcm,3,Overextension of segmentation into muscle on right side. Liver is adequately segmented in the majority of slices.,,
-ne1,FALSE,tcga-lihc,TCGA-G3-AAV4,20060509,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377009651524040310426,1.3.6.1.4.1.14519.5.2.1.1079.4008.142961586635199319886915449801,ai_TCGA-G3-AAV4_20060509_0.seg.dcm,2,Portions of the liver are not included in many slices. No irrelevant segmentation noted.,,
-ne1,FALSE,tcga-lihc,TCGA-G3-AAV5,20070617,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.328820149586454940911187796566,1.3.6.1.4.1.14519.5.2.1.1079.4008.238916013234650527158474021661,ai_TCGA-G3-AAV5_20070617_0.seg.dcm,2,The entire liver is not adequately segmented in the bottom and top slices.,,
-ne1,FALSE,tcga-lihc,TCGA-G3-AAV5,20070617,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.328820149586454940911187796566,1.3.6.1.4.1.14519.5.2.1.1079.4008.890245884342565506737304555717,ai_TCGA-G3-AAV5_20070617_1.seg.dcm,1,Liver not segmented. Only irrelevant segmentation voxels noted.,,
-ne1,FALSE,tcga-lihc,TCGA-K7-A5RG,20000329,0,1.3.6.1.4.1.14519.5.2.1.3983.4008.335698561314795862034766172231,1.3.6.1.4.1.14519.5.2.1.3983.4008.339131617904721012312015165341,ai_TCGA-K7-A5RG_20000329_0.seg.dcm,4,Liver adequately segmented in the majority of slices. In the top slices the segmentation region can be extended outwards slightly.,,
+Reviewer,Validation,Collection,PatientID,StudyDate,StudyDate_suffix,StudyInstanceUID,SeriesInstanceUID,AISegmentation,LikertScore,CommentsAboutAISegmentation,CommentsAboutScan,CorrectedSegmentation
+rad1,TRUE,tcga-lihc,TCGA-DD-A1EH,20010629,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.150731818010634307938925068434,ai_TCGA-DD-A1EH_20010629_1.seg.dcm,5,no corrections to segmentation,,
+rad1,TRUE,tcga-lihc,TCGA-DD-A1EK,20010531,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.145559228873850789419206611274,1.3.6.1.4.1.14519.5.2.1.3344.4008.141679692047619086484827875122,ai_TCGA-DD-A1EK_20010531_0.seg.dcm,4,,,rad1_TCGA-DD-A1EK_20010531_0.seg.dcm
+rad1,TRUE,tcga-lihc,TCGA-DD-A1EL,20020116,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.158425276417455187125782049563,1.3.6.1.4.1.14519.5.2.1.3344.4008.269121785256966585574860555086,ai_TCGA-DD-A1EL_20020116_0.seg.dcm,4,,,rad1_TCGA-DD-A1EL_20020116_0.seg.dcm
+rad1,TRUE,tcga-lihc,TCGA-DD-A4NK,19980326,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.153767944691753512045004643297,1.3.6.1.4.1.14519.5.2.1.3344.4008.298723806090585681738761928284,ai_TCGA-DD-A4NK_19980326_0.seg.dcm,5,no change in segmentation,,
+rad1,TRUE,tcga-lihc,TCGA-DD-A4NL,20000323,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.123435557800692096834678469001,1.3.6.1.4.1.14519.5.2.1.3344.4008.295183335434779171346930899761,ai_TCGA-DD-A4NL_20000323_0.seg.dcm,5,No change to segmentation,,
+rad1,TRUE,tcga-lihc,TCGA-DD-A4NS,20001011,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.283574261555925310865604827346,ai_TCGA-DD-A4NS_20001011_2.seg.dcm,4,few edits,,rad1_TCGA-DD-A4NS_20001011_2.seg.dcm
+rad1,TRUE,tcga-lihc,TCGA-DD-A4NV,20050902,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.989114914849856678473558553333,1.3.6.1.4.1.14519.5.2.1.3344.4008.288401067690076379005498612621,ai_TCGA-DD-A4NV_20050902_0.seg.dcm,5,no changes to segmentation,,
+rad1,TRUE,tcga-lihc,TCGA-G3-A5SK,20050701,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.291273592266846387761210025624,1.3.6.1.4.1.14519.5.2.1.1079.4008.318266519014308109379919043732,ai_TCGA-G3-A5SK_20050701_0.seg.dcm,3,,,rad1_TCGA-G3-A5SK_20050701_0.seg.dcm
+rad1,TRUE,tcga-lihc,TCGA-G3-A6UC,20060610,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.273569008619736834081366735502,1.3.6.1.4.1.14519.5.2.1.1079.4008.972951195130883284724850114185,ai_TCGA-G3-A6UC_20060610_0.seg.dcm,5,no change to segmentation,,
+ne1,TRUE,tcga-lihc,TCGA-DD-A1EH,20010629,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.150731818010634307938925068434,ai_TCGA-DD-A1EH_20010629_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,TRUE,tcga-lihc,TCGA-DD-A1EK,20010531,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.145559228873850789419206611274,1.3.6.1.4.1.14519.5.2.1.3344.4008.141679692047619086484827875122,ai_TCGA-DD-A1EK_20010531_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,TRUE,tcga-lihc,TCGA-DD-A1EL,20020116,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.158425276417455187125782049563,1.3.6.1.4.1.14519.5.2.1.3344.4008.269121785256966585574860555086,ai_TCGA-DD-A1EL_20020116_0.seg.dcm,3,Did not fully segment liver in first few slices. Needed adjustment.,,ne1_TCGA-DD-A1EL_20020116_0.seg.dcm
+ne1,TRUE,tcga-lihc,TCGA-DD-A4NK,19980326,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.153767944691753512045004643297,1.3.6.1.4.1.14519.5.2.1.3344.4008.298723806090585681738761928284,ai_TCGA-DD-A4NK_19980326_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,TRUE,tcga-lihc,TCGA-DD-A4NL,20000323,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.123435557800692096834678469001,1.3.6.1.4.1.14519.5.2.1.3344.4008.295183335434779171346930899761,ai_TCGA-DD-A4NL_20000323_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,TRUE,tcga-lihc,TCGA-DD-A4NS,20001011,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.283574261555925310865604827346,ai_TCGA-DD-A4NS_20001011_2.seg.dcm,2,AI did not segment the entire liver in the majority of slices where it was present. Adjustment needed.,,ne1_TCGA-DD-A4NS_20001011_2.seg.dcm
+ne1,TRUE,tcga-lihc,TCGA-DD-A4NV,20050902,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.989114914849856678473558553333,1.3.6.1.4.1.14519.5.2.1.3344.4008.288401067690076379005498612621,ai_TCGA-DD-A4NV_20050902_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,TRUE,tcga-lihc,TCGA-G3-A5SK,20050701,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.291273592266846387761210025624,1.3.6.1.4.1.14519.5.2.1.1079.4008.318266519014308109379919043732,ai_TCGA-G3-A5SK_20050701_0.seg.dcm,3,"Many slices needed adjustment as either the entire liver was not included or far too much area was included. AI was able, however, to segment the majority of the liver in each slice.",,ne1_TCGA-G3-A5SK_20050701_0.seg.dcm
+ne1,TRUE,tcga-lihc,TCGA-G3-A6UC,20060610,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.273569008619736834081366735502,1.3.6.1.4.1.14519.5.2.1.1079.4008.972951195130883284724850114185,ai_TCGA-G3-A6UC_20060610_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-BC-A10Z,19931220,0,1.3.6.1.4.1.14519.5.2.1.8421.4008.338750756821159002165351451130,1.3.6.1.4.1.14519.5.2.1.8421.4008.761093011533106086639756339870,ai_TCGA-BC-A10Z_19931220_0.seg.dcm,5,Accurate segmentation of liver. No adjustment necessary.,,
+ne1,FALSE,tcga-lihc,TCGA-BC-A3KF,20020218,0,1.3.6.1.4.1.14519.5.2.1.8421.4008.212149247533463904570758018240,1.3.6.1.4.1.14519.5.2.1.8421.4008.172664185005829912144086258851,ai_TCGA-BC-A3KF_20020218_0.seg.dcm,4,Adequate segmentation of liver. Some overextension of segmentation region in few of the top slides.,,
+ne1,FALSE,tcga-lihc,TCGA-BC-A69H,20030527,1,1.3.6.1.4.1.14519.5.2.1.8421.4008.232432715274855073157517341855,1.3.6.1.4.1.14519.5.2.1.8421.4008.220393017361571445089925754168,ai_TCGA-BC-A69H_20030527_1.seg.dcm,5,Accurate segmentation of liver. No adjustment necessary.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A115,19960329,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.273311785136119701888483417354,1.3.6.1.4.1.14519.5.2.1.3344.4008.334650040258165055970898598044,ai_TCGA-DD-A115_19960329_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A115,19960512,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.111190381797398190611685670115,1.3.6.1.4.1.14519.5.2.1.3344.4008.161980713684483394055711266086,ai_TCGA-DD-A115_19960512_0.seg.dcm,4,"Liver adequately segmented. Shape of segmentation area in top slides is not perfect, but adjustment is not super necessary.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A116,19960314,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.379064229494090410137584103828,1.3.6.1.4.1.14519.5.2.1.3344.4008.135494304833236144987187674002,ai_TCGA-DD-A116_19960314_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A11A,19970706,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.561613401719690898408844572706,1.3.6.1.4.1.14519.5.2.1.3344.4008.391094057748793176042775715108,ai_TCGA-DD-A11A_19970706_0.seg.dcm,2,"Over-segmentation of region directly adjacent to liver in the majority of slices where the liver is present. Segmentation needs correction. In only a handful of the bottom slices, the liver is segmented adequately.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A11C,19981028,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.229699852250561373965265745922,1.3.6.1.4.1.14519.5.2.1.3344.4008.114015423829654026617391168242,ai_TCGA-DD-A11C_19981028_2.seg.dcm,4,Liver is adequately segmented. The segmentation area can be increased outwards in the last few slices on the top.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A11D,20000913,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.213393886593375153276050327721,1.3.6.1.4.1.14519.5.2.1.3344.4008.312604581201752148340133119341,ai_TCGA-DD-A11D_20000913_0.seg.dcm,4,"Liver is adequately segmented. A few of the middle slices do not cover the entirety of the liver, but it is a very small portion.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1E9,19950906,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.824746819228131664143570751388,1.3.6.1.4.1.14519.5.2.1.3344.4008.208949456379783541277979256617,ai_TCGA-DD-A1E9_19950906_0.seg.dcm,3,"For the most part, the liver is adequately segmented. There are a few slices where it can be adjusted. The spleen also appears to be segmented in numerous slices.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EH,20010227,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.114918398611791628520770797703,1.3.6.1.4.1.14519.5.2.1.3344.4008.528527982325292531978902370574,ai_TCGA-DD-A1EH_20010227_0.seg.dcm,4,Adequate segmentation of liver. Some overextension of segmentation region in few of the middle slices.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EK,20010609,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.103259826517251415276965927375,1.3.6.1.4.1.14519.5.2.1.3344.4008.162751390639576496099577915901,ai_TCGA-DD-A1EK_20010609_0.seg.dcm,3,"AI was able to identify and segment liver, but there are many slices with gaps inside the liver region that need adjustment.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EL,20020605,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.143224773631509814038880620336,1.3.6.1.4.1.14519.5.2.1.3344.4008.265501579320933065719451395150,ai_TCGA-DD-A1EL_20020605_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NA,20020110,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.160220435632848389630559335170,1.3.6.1.4.1.14519.5.2.1.3344.4008.374038589141774665244999247652,ai_TCGA-DD-A4NA_20020110_1.seg.dcm,4,Adequate segmentation of liver. Some overextension of segmentation region in few of the top slices.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4ND,19980101,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.663582373061408434045464445433,1.3.6.1.4.1.14519.5.2.1.3344.4008.122333400409451122879233480510,ai_TCGA-DD-A4ND_19980101_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4ND,19980103,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.141621615283764837404351584644,1.3.6.1.4.1.14519.5.2.1.3344.4008.240151200781717301848509985012,ai_TCGA-DD-A4ND_19980103_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4ND,19991229,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.294765947948814153890689694139,1.3.6.1.4.1.14519.5.2.1.3344.4008.144208613011399899455044558161,ai_TCGA-DD-A4ND_19991229_0.seg.dcm,4,Liver adequately segmented. There are some random segmentation voxels in the center of the image in a few of the top slices (irrespective to the liver).,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NE,20030830,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.217069790726612801841206031642,1.3.6.1.4.1.14519.5.2.1.3344.4008.309006815412086883143504283501,ai_TCGA-DD-A4NE_20030830_1.seg.dcm,2,"Coronal and sagittal image quality not great. In a few slices, AI segmented irrelevant organs or did not fully cover the entire liver.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NE,20040319,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.149922237131000847016153692128,1.3.6.1.4.1.14519.5.2.1.3344.4008.882557897404715844683325639915,ai_TCGA-DD-A4NE_20040319_0.seg.dcm,5,,Liver accurately segmented. No adjustment necessary,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NG,20020517,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.233811296314642551471614789062,1.3.6.1.4.1.14519.5.2.1.3344.4008.313796055651915079436969192808,ai_TCGA-DD-A4NG_20020517_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NG,20030911,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.277011436751126552397576927159,1.3.6.1.4.1.14519.5.2.1.3344.4008.460919767301516578653712060606,ai_TCGA-DD-A4NG_20030911_1.seg.dcm,4,Liver adequately segmented. There are a few slices where the segmentation area can be increased slightly.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NI,20020216,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.302612097544963316088092090676,1.3.6.1.4.1.14519.5.2.1.3344.4008.224343227507905277467666606717,ai_TCGA-DD-A4NI_20020216_0.seg.dcm,5,Liver accurately segmented. Little to no adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NI,20040521,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.175826608307498311132229109398,1.3.6.1.4.1.14519.5.2.1.3344.4008.287212806515445440668495171006,ai_TCGA-DD-A4NI_20040521_1.seg.dcm,3,Liver for the most part adequately segmented. There are a few slices where the bottom of the liver are not included in the segmentation (this is most visible in the coronal view) or areas of the liver in general are not included.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20040227,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.177316748388312779271301192905,1.3.6.1.4.1.14519.5.2.1.3344.4008.115264785656904542056592486066,ai_TCGA-DD-A4NJ_20040227_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20050324,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.178571847081305757756952090882,1.3.6.1.4.1.14519.5.2.1.3344.4008.571653965400772551680793228390,ai_TCGA-DD-A4NJ_20050324_1.seg.dcm,4,The liver is adequately segmented. There are a few slices where the segmentation can be extended outwards slightly.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20051015,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.151511465574060586762887334921,1.3.6.1.4.1.14519.5.2.1.3344.4008.476340434793464019964427888823,ai_TCGA-DD-A4NJ_20051015_1.seg.dcm,4,The liver is adequately segmented. There are a few slices where the segmentation can increase outwards slightly.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20000707,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.283305018370962482092363755149,1.3.6.1.4.1.14519.5.2.1.3344.4008.698509830523414701636495235205,ai_TCGA-DD-A4NL_20000707_2.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010104,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.295852698560353190326329207249,1.3.6.1.4.1.14519.5.2.1.3344.4008.965531043212955882044413761437,ai_TCGA-DD-A4NL_20010104_2.seg.dcm,3,The liver is for the most part segmented adequately. There are random segmentation voxels in irrelevant organs or in the breast tissue in the first few slices. There are also a few slices where the entire liver is not fully included in the segmentation.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010711,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.217381303091455644784847397746,1.3.6.1.4.1.14519.5.2.1.3344.4008.252117527833266466109320709457,ai_TCGA-DD-A4NL_20010711_2.seg.dcm,2,Liver not fully segmented in many slices. Large portions of liver excluded.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20011211,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.924229943132737214631304598948,1.3.6.1.4.1.14519.5.2.1.3344.4008.109254970283977303397073728772,ai_TCGA-DD-A4NL_20011211_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NN,19970122,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.334960828217735997056926407680,1.3.6.1.4.1.14519.5.2.1.3344.4008.398727212485699636033896987425,ai_TCGA-DD-A4NN_19970122_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NN,19970709,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.238875024531656123348758022775,1.3.6.1.4.1.14519.5.2.1.3344.4008.207848934776528286087485176422,ai_TCGA-DD-A4NN_19970709_0.seg.dcm,4,"The liver is adequately segmented. In a few of the top slices, the segmentation can increase outwards slightly.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20050525,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.226816019260191889484839567314,1.3.6.1.4.1.14519.5.2.1.3344.4008.508067462958962930439058636497,ai_TCGA-DD-A4NO_20050525_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20060103,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.759986268611549722078244299409,1.3.6.1.4.1.14519.5.2.1.3344.4008.218404660389610360651678437867,ai_TCGA-DD-A4NO_20060103_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NQ,20000129,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.194045555077154079746429047642,1.3.6.1.4.1.14519.5.2.1.3344.4008.224532092086806107041379081405,ai_TCGA-DD-A4NQ_20000129_0.seg.dcm,4,"Liver adequately segmented. In the first few bottom slices, there are some irrelevant extraneous segmentation voxels in other organs.",,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25S,20020915,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.117265092232754988757648971195,1.3.6.1.4.1.14519.5.2.1.1079.4008.186532305717125520774895527018,ai_TCGA-G3-A25S_20020915_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25U,20010216,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.405829857382526369105550547365,1.3.6.1.4.1.14519.5.2.1.1079.4008.142033825293496813496372398694,ai_TCGA-G3-A25U_20010216_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20000428,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.230977793203918304163491543099,1.3.6.1.4.1.14519.5.2.1.1079.4008.161656825189547046825817060109,ai_TCGA-G3-A25Y_20000428_1.seg.dcm,3,"Liver adequately segmented in the majority of slices. In the first few bottom slices, the entire liver area is not included in the segmentation.",,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20070617,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.207384642619260022321262977167,1.3.6.1.4.1.14519.5.2.1.1079.4008.747511138638455991207658889563,ai_TCGA-G3-A25Y_20070617_1.seg.dcm,3,"Liver is adequately segmented in the majority of slices. In a few of the top slices, the entire area of the liver is not included.",,
+ne1,FALSE,tcga-lihc,TCGA-G3-A3CH,20040507,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.116712770482771075241626231125,1.3.6.1.4.1.14519.5.2.1.1079.4008.275816260983984341641329560754,ai_TCGA-G3-A3CH_20040507_1.seg.dcm,4,"Liver adequately segmented. In some of the bottom slices, the entire liver is not included but this is a very small portion.",,
+ne1,FALSE,tcga-lihc,TCGA-G3-A3CI,20040715,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.324693330665701767725603699315,1.3.6.1.4.1.14519.5.2.1.1079.4008.118291050558345675291995888259,ai_TCGA-G3-A3CI_20040715_0.seg.dcm,2,"Irrelevant segmentation voxels in bottom slide. Liver identified by AI, but not fully segmented in many slides.",,
+ne1,FALSE,tcga-lihc,TCGA-G3-AAUZ,20051108,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.245846761312718553858837754385,1.3.6.1.4.1.14519.5.2.1.1079.4008.114832936145958292789896133590,ai_TCGA-G3-AAUZ_20051108_0.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-G3-AAV4,20060509,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377009651524040310426,1.3.6.1.4.1.14519.5.2.1.1079.4008.153956053674628255368772115120,ai_TCGA-G3-AAV4_20060509_1.seg.dcm,5,Liver accurately segmented. No adjustment necessary,,
+ne1,FALSE,tcga-lihc,TCGA-G3-AAV5,20070617,2,1.3.6.1.4.1.14519.5.2.1.1079.4008.328820149586454940911187796566,1.3.6.1.4.1.14519.5.2.1.1079.4008.161686441012277956341337038260,ai_TCGA-G3-AAV5_20070617_2.seg.dcm,1,"Irrelevant organs segmented. In the majority of the slices where it is present, the entire area of the liver is not included. Needs major adjustment.",,
+ne1,FALSE,tcga-lihc,TCGA-K7-A5RG,20000329,1,1.3.6.1.4.1.14519.5.2.1.3983.4008.335698561314795862034766172231,1.3.6.1.4.1.14519.5.2.1.3983.4008.224387668563746005693412217402,ai_TCGA-K7-A5RG_20000329_1.seg.dcm,4,"The liver is adequately segmented. In some of the top slices, the segmentation area can be increased outwards slightly.",,
+ne1,FALSE,tcga-lihc,TCGA-BC-A69H,20030527,0,1.3.6.1.4.1.14519.5.2.1.8421.4008.232432715274855073157517341855,1.3.6.1.4.1.14519.5.2.1.8421.4008.128712245047293209552994910188,ai_TCGA-BC-A69H_20030527_0.seg.dcm,4,"Adequate segmentation of liver. In a few of the top slices, the entire liver area is not included (segmentation area can be increased outwards slightly).",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A11C,19981028,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.229699852250561373965265745922,1.3.6.1.4.1.14519.5.2.1.3344.4008.163004960475005676802829596817,ai_TCGA-DD-A11C_19981028_0.seg.dcm,2,AI misses large portion of liver in many of the top slices.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A11C,19981028,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.229699852250561373965265745922,1.3.6.1.4.1.14519.5.2.1.3344.4008.115602691812578451725224669740,ai_TCGA-DD-A11C_19981028_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EH,20010629,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.336451395223174621319606562381,ai_TCGA-DD-A1EH_20010629_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EH,20010629,2,1.3.6.1.4.1.14519.5.2.1.3344.4008.266568840516598684200293764710,1.3.6.1.4.1.14519.5.2.1.3344.4008.121901765028698957794203464452,ai_TCGA-DD-A1EH_20010629_2.seg.dcm,3,"Liver is adequately segmented in most slices. In the first few bottom slices where the liver is present, the segmentation region is awkwardly cut off towards the bottom left region of liver.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EL,20020116,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.158425276417455187125782049563,1.3.6.1.4.1.14519.5.2.1.3344.4008.153373588899215893192917369310,ai_TCGA-DD-A1EL_20020116_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A1EL,20020605,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.143224773631509814038880620336,1.3.6.1.4.1.14519.5.2.1.3344.4008.137417991108008645521008541818,ai_TCGA-DD-A1EL_20020605_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NA,20020110,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.160220435632848389630559335170,1.3.6.1.4.1.14519.5.2.1.3344.4008.172222876443325136473137847444,ai_TCGA-DD-A4NA_20020110_0.seg.dcm,4,"AI adequately segmented liver in almost all slices. In a few of the top slices, the AI left out small portions of the liver. ",Image quality is somewhat grainy.,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NE,20030830,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.217069790726612801841206031642,1.3.6.1.4.1.14519.5.2.1.3344.4008.504589444775630863861688726200,ai_TCGA-DD-A4NE_20030830_0.seg.dcm,4,"Liver adequately segmented. In a few of the bottom slices, the entire area of the liver is not included. Segmentation can be extended outwards slightly.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NG,20030911,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.277011436751126552397576927159,1.3.6.1.4.1.14519.5.2.1.3344.4008.136119724780535778270649472484,ai_TCGA-DD-A4NG_20030911_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NI,20040521,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.175826608307498311132229109398,1.3.6.1.4.1.14519.5.2.1.3344.4008.230419527952461164804294066822,ai_TCGA-DD-A4NI_20040521_0.seg.dcm,3,Liver is adequately segmented in a majority of slices. AI appears to segment half of a portal vein in the first few bottom slices.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20050324,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.178571847081305757756952090882,1.3.6.1.4.1.14519.5.2.1.3344.4008.127004125990655063673320541498,ai_TCGA-DD-A4NJ_20050324_0.seg.dcm,4,"Adequate segmentation of liver. In a few of the bottom slices, the top portion of the liver (from the axial view) is not included.",,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NJ,20051015,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.151511465574060586762887334921,1.3.6.1.4.1.14519.5.2.1.3344.4008.184445494707565935416050657382,ai_TCGA-DD-A4NJ_20051015_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20000707,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.283305018370962482092363755149,1.3.6.1.4.1.14519.5.2.1.3344.4008.141557981276332362184646880847,ai_TCGA-DD-A4NL_20000707_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20000707,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.283305018370962482092363755149,1.3.6.1.4.1.14519.5.2.1.3344.4008.223720200659092258881103564649,ai_TCGA-DD-A4NL_20000707_1.seg.dcm,2,AI missed large portions of the liver in many slices. Also included a portion of irrelevant muscle in a few of the bottom slices.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010104,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.295852698560353190326329207249,1.3.6.1.4.1.14519.5.2.1.3344.4008.174933394548192471772029827767,ai_TCGA-DD-A4NL_20010104_0.seg.dcm,2,AI missed large portions of the liver in many slices. Irrelevant segmentation of right kidney and portion of chest muscle.,Image quality is grainy.,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010104,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.295852698560353190326329207249,1.3.6.1.4.1.14519.5.2.1.3344.4008.217630104813016126487750234080,ai_TCGA-DD-A4NL_20010104_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010711,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.217381303091455644784847397746,1.3.6.1.4.1.14519.5.2.1.3344.4008.477051078981473110616142832395,ai_TCGA-DD-A4NL_20010711_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20010711,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.217381303091455644784847397746,1.3.6.1.4.1.14519.5.2.1.3344.4008.101836454235477604240412504215,ai_TCGA-DD-A4NL_20010711_1.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NL,20011211,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.924229943132737214631304598948,1.3.6.1.4.1.14519.5.2.1.3344.4008.177491661724537296017407228479,ai_TCGA-DD-A4NL_20011211_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NN,19970122,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.334960828217735997056926407680,1.3.6.1.4.1.14519.5.2.1.3344.4008.461026143927990958589293196591,ai_TCGA-DD-A4NN_19970122_0.seg.dcm,4,"Adequate segmentation of liver in all slices. In the first two to three bottom slices where the liver first emerges, the entire liver volume is not included.",Somewhat grainy image quality for coronal and sagittal slices.,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20050525,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.226816019260191889484839567314,1.3.6.1.4.1.14519.5.2.1.3344.4008.199400007458737404101255479212,ai_TCGA-DD-A4NO_20050525_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NO,20060103,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.759986268611549722078244299409,1.3.6.1.4.1.14519.5.2.1.3344.4008.479401360926084327281964656390,ai_TCGA-DD-A4NO_20060103_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NS,20001011,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.200842643214043585407342107350,ai_TCGA-DD-A4NS_20001011_0.seg.dcm,2,AI missed large portions of liver in many slices. Also appears to segment spleen in some slices as well.,Image quality is grainy,
+ne1,FALSE,tcga-lihc,TCGA-DD-A4NS,20001011,1,1.3.6.1.4.1.14519.5.2.1.3344.4008.991079474891517478089231243952,1.3.6.1.4.1.14519.5.2.1.3344.4008.206559987112338396022163503109,ai_TCGA-DD-A4NS_20001011_1.seg.dcm,3,"Adequate segmentation of liver in majority of slices. In some, the outer edge of the liver segmentation is not perfect, but it suffices. Irrelevant segmentation of spleen in some slices.",,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25U,20010216,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.405829857382526369105550547365,1.3.6.1.4.1.14519.5.2.1.1079.4008.108655443343069434585549901126,ai_TCGA-G3-A25U_20010216_0.seg.dcm,2,Large portion of liver left out of segmentation in many of the bottom slices. Segmentation is adequate as it goes to the top slices.,Grainy image quality.,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20000428,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.230977793203918304163491543099,1.3.6.1.4.1.14519.5.2.1.1079.4008.218353931327309701484061486682,ai_TCGA-G3-A25Y_20000428_0.seg.dcm,1,No segmentation of liver.,Coronal and sagittal images cut off when uploading to 3D slicer.,
+ne1,FALSE,tcga-lihc,TCGA-G3-A25Y,20070617,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.207384642619260022321262977167,1.3.6.1.4.1.14519.5.2.1.1079.4008.197655373334398563370393733433,ai_TCGA-G3-A25Y_20070617_0.seg.dcm,4,Liver adequately segmented in the majority of slices. There are some slices where the segmentation region can be extended outwards slightly.,,
+ne1,FALSE,tcga-lihc,TCGA-G3-A3CH,20040507,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.116712770482771075241626231125,1.3.6.1.4.1.14519.5.2.1.1079.4008.465478383053845803437953385013,ai_TCGA-G3-A3CH_20040507_0.seg.dcm,2,Irrelevant segmentation of muscle. AI did not accurately segment the liver.,,
+ne1,FALSE,tcga-lihc,TCGA-G3-A5SK,20050701,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.291273592266846387761210025624,1.3.6.1.4.1.14519.5.2.1.1079.4008.138620238014948298034813730954,ai_TCGA-G3-A5SK_20050701_1.seg.dcm,3,Overextension of segmentation into muscle on right side. Liver is adequately segmented in the majority of slices.,,
+ne1,FALSE,tcga-lihc,TCGA-G3-AAV4,20060509,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377009651524040310426,1.3.6.1.4.1.14519.5.2.1.1079.4008.142961586635199319886915449801,ai_TCGA-G3-AAV4_20060509_0.seg.dcm,2,Portions of the liver are not included in many slices. No irrelevant segmentation noted.,,
+ne1,FALSE,tcga-lihc,TCGA-G3-AAV5,20070617,0,1.3.6.1.4.1.14519.5.2.1.1079.4008.328820149586454940911187796566,1.3.6.1.4.1.14519.5.2.1.1079.4008.238916013234650527158474021661,ai_TCGA-G3-AAV5_20070617_0.seg.dcm,2,The entire liver is not adequately segmented in the bottom and top slices.,,
+ne1,FALSE,tcga-lihc,TCGA-G3-AAV5,20070617,1,1.3.6.1.4.1.14519.5.2.1.1079.4008.328820149586454940911187796566,1.3.6.1.4.1.14519.5.2.1.1079.4008.890245884342565506737304555717,ai_TCGA-G3-AAV5_20070617_1.seg.dcm,1,Liver not segmented. Only irrelevant segmentation voxels noted.,,
+ne1,FALSE,tcga-lihc,TCGA-K7-A5RG,20000329,0,1.3.6.1.4.1.14519.5.2.1.3983.4008.335698561314795862034766172231,1.3.6.1.4.1.14519.5.2.1.3983.4008.339131617904721012312015165341,ai_TCGA-K7-A5RG_20000329_0.seg.dcm,4,Liver adequately segmented in the majority of slices. In the top slices the segmentation region can be extended outwards slightly.,,
 ne1,FALSE,tcga-lihc,TCGA-DD-A4NF,20040221,0,1.3.6.1.4.1.14519.5.2.1.3344.4008.107965720766540992943455005441,1.3.6.1.4.1.14519.5.2.1.3344.4008.320236598776398707256884092317,ai_TCGA-DD-A4NF_20040221_0.seg.dcm,5,Accurate segmentation of liver. No adjustment needed.,,
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