adjusting_cerebrum_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Master script to run the one shot analysis of the microbleeds in a single go</h1>\n",
    "<h3>Intended to be used to collect the target labelled data(CMB patches) from all the subjects and store it as train & test data in a pandas dataframe</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Working Directory: /Users/shwetankpanwar/Documents/BTPData/python_scripts/cmb_segmentation\n",
      "The file structure creation failed. Structure already exists!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from pfca import init_path\n",
    "from pfca.visuals.img2D import imgplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pfca.core.preprocessing import *\n",
    "from pfca.core.processing import *\n",
    "from pfca import file_read as f\n",
    "import ants\n",
    "import skimage.feature as feature\n",
    "from time import time\n",
    "from pfca.exp.results import roi_snipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pfca.exp.dataset import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_dir, nifti_dir = init_path()\n",
    "cur_path = os.getcwd()\n",
    "patients_list = f.get_immediate_subdirectories(raw_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_management()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing for template exclusion idea for anatomical exclusion to reduce search space\n",
    "talariach = ants.image_read(cur_path + '/datasets/talairach.nii')\n",
    "mni_t2 = ants.image_read(cur_path + '/datasets/mni_t2_template.nii')\n",
    "harvard_atlas = ants.image_read(cur_path + '/datasets/harvard_atlas.nii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Registering Harvard-Oxford Atlas with MNI-152 template using non-linear registration\n",
    "harvard_mni = mni_template_registration(cur_path,harvard_atlas,'harvard')\n",
    "#generating cortex mask from the atlas excluding all the regions of cerebrum \n",
    "harvard_mni_mask = ((harvard_mni.numpy()) > 0)*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['p2', 'p1']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Data preprocesssing begins...\")\n",
    "for patient in patients_list:\n",
    "    file_list = f.dcm_lst(raw_dir+patient)\n",
    "    eswan_list = f.get_label_based_list(file_list,'eSWAN')\n",
    "    img_eSWAN = f.extract_dcm_array(eswan_list)\n",
    "    dcm_to_nifti(img_eSWAN,patient, nifti_dir, eswan_list, \n",
    "             rotation = True)\n",
    "    skull_stripping(nifti_dir,patient+'_conv')\n",
    "    print(\"/n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " RST processing on p2 initiated!\n",
      "Time taken for 3D RST: 20.665857791900635\n",
      "ROIs saved to dataset successfully!\n",
      "\n",
      " RST processing on p1 initiated!\n",
      "Time taken for 3D RST: 20.05469799041748\n",
      "ROIs saved to dataset successfully!\n"
     ]
    }
   ],
   "source": [
    "for patient in patients_list:\n",
    "    print(\"\\n RST processing on \" + str(patient) + \" initiated!\")\n",
    "    im_nifti = nifti_ANTS(nifti_dir, patient, category = 'eswan', unskulled = True)\n",
    "    im_patient = mni_template_registration(cur_path, im_nifti, patient)\n",
    "    #calculating the RST output on the whole image\n",
    "    img_pat = im_patient.numpy()\n",
    "    rst_whole = rst_3d(img_pat, 2,2,0.2)\n",
    "    rst_masked = rst_whole * harvard_mni_mask\n",
    "    #Extracting the candidate points from the RST output image\n",
    "    peaks = feature.peak_local_max(rst_masked, min_distance=2,threshold_rel = 0.2)\n",
    "    snips = roi_snipping(img_pat,peaks,5)\n",
    "    dataset.append(patient, peaks, snips)\n",
    "    print(\"ROIs saved to dataset successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[97, 91, 97]</td>\n",
       "      <td>[[[1520.5015, 1569.4806, 1601.607, 1616.3756, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[93, 80, 128]</td>\n",
       "      <td>[[[1512.5273, 1519.9901, 1524.0062, 1534.6393,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[84, 74, 85]</td>\n",
       "      <td>[[[1526.3732, 1481.858, 1486.6381, 1509.9438, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[53, 63, 96]</td>\n",
       "      <td>[[[1575.6156, 1599.6085, 1593.7793, 1564.5856,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[135, 100, 91]</td>\n",
       "      <td>[[[1719.8491, 1681.455, 1653.2905, 1628.386, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[62, 104, 58]</td>\n",
       "      <td>[[[1569.3444, 1493.1567, 1404.4259, 1315.8666,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RST_peak                                        image_patch label  \\\n",
       "0    [97, 91, 97]  [[[1520.5015, 1569.4806, 1601.607, 1616.3756, ...   NaN   \n",
       "1   [93, 80, 128]  [[[1512.5273, 1519.9901, 1524.0062, 1534.6393,...   NaN   \n",
       "2    [84, 74, 85]  [[[1526.3732, 1481.858, 1486.6381, 1509.9438, ...   NaN   \n",
       "3    [53, 63, 96]  [[[1575.6156, 1599.6085, 1593.7793, 1564.5856,...   NaN   \n",
       "4  [135, 100, 91]  [[[1719.8491, 1681.455, 1653.2905, 1628.386, 1...   NaN   \n",
       "5   [62, 104, 58]  [[[1569.3444, 1493.1567, 1404.4259, 1315.8666,...   NaN   \n",
       "\n",
       "  patient_name  \n",
       "0           p2  \n",
       "1           p2  \n",
       "2           p2  \n",
       "3           p2  \n",
       "4           p1  \n",
       "5           p1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.disp_roi('p1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset.disp_roi('p2')\n",
    "datafile = dataset.show()\n",
    "datafile.to_pickle('rst_output') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_pickle('rst_output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = dataset_management()\n",
    "t.dataset = data\n",
    "t.disp_roi('p1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_data = pd.read_pickle('rst_output_all_subjects_except_8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[171, 103, 87]</td>\n",
       "      <td>[[[1604.2019, 1612.2238, 1584.8153, 1550.3676,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[171, 97, 61]</td>\n",
       "      <td>[[[1151.8575, 1269.8489, 1393.6145, 1520.887, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[170, 121, 58]</td>\n",
       "      <td>[[[1769.2383, 1805.1096, 1834.2352, 1837.5428,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[170, 117, 71]</td>\n",
       "      <td>[[[1762.8202, 1846.0253, 1921.8511, 1976.3429,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[170, 102, 53]</td>\n",
       "      <td>[[[710.26697, 800.9684, 878.76276, 935.9742, 9...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[170, 91, 84]</td>\n",
       "      <td>[[[1851.205, 1951.2527, 1994.4828, 1983.8191, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[169, 115, 97]</td>\n",
       "      <td>[[[1621.4022, 1678.7334, 1741.5708, 1791.8369,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[168, 115, 105]</td>\n",
       "      <td>[[[1761.327, 1798.1428, 1875.3728, 1971.9221, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[168, 112, 88]</td>\n",
       "      <td>[[[1596.7714, 1608.4497, 1636.7616, 1668.0872,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[168, 83, 73]</td>\n",
       "      <td>[[[2091.7402, 2410.6912, 2583.867, 2566.7422, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[167, 128, 97]</td>\n",
       "      <td>[[[1772.9629, 1727.7701, 1703.4581, 1691.2244,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[167, 80, 85]</td>\n",
       "      <td>[[[1992.0345, 2051.3005, 2069.7288, 2123.867, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[166, 131, 69]</td>\n",
       "      <td>[[[1488.9651, 1445.1831, 1432.5006, 1435.0431,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[166, 91, 112]</td>\n",
       "      <td>[[[1683.0985, 1633.1614, 1608.1573, 1595.7361,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[165, 73, 68]</td>\n",
       "      <td>[[[733.609, 843.3025, 961.64136, 1040.1997, 10...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[164, 133, 43]</td>\n",
       "      <td>[[[2062.04, 2301.349, 2410.9116, 2411.8052, 23...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[161, 132, 40]</td>\n",
       "      <td>[[[1227.8506, 1212.9775, 1141.3005, 1080.7461,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[156, 150, 64]</td>\n",
       "      <td>[[[1687.2054, 1651.17, 1615.944, 1622.3024, 16...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[153, 132, 25]</td>\n",
       "      <td>[[[0.0, 238.51714, 469.1484, 541.24243, 595.72...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[150, 52, 92]</td>\n",
       "      <td>[[[1644.8794, 1458.6715, 1234.8123, 1018.749, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[148, 159, 54]</td>\n",
       "      <td>[[[1227.709, 1333.4268, 1406.8408, 1442.3579, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[148, 133, 58]</td>\n",
       "      <td>[[[2012.3528, 1982.8635, 2000.4832, 2001.8976,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[148, 53, 102]</td>\n",
       "      <td>[[[1427.9668, 1233.3207, 991.95715, 728.41437,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[147, 182, 50]</td>\n",
       "      <td>[[[0.0, 0.0, 119.71727, 552.3697, 936.203, 114...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[146, 102, 40]</td>\n",
       "      <td>[[[302.26312, 190.05719, 170.38731, 469.99728,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[146, 45, 73]</td>\n",
       "      <td>[[[705.09674, 721.1772, 756.4793, 769.5555, 74...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[145, 51, 106]</td>\n",
       "      <td>[[[725.55707, 475.2307, 319.07086, 248.6426, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[144, 195, 61]</td>\n",
       "      <td>[[[1597.6909, 1786.7861, 1764.0107, 1727.4783,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[144, 49, 102]</td>\n",
       "      <td>[[[793.2391, 650.29694, 499.59818, 400.52652, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[142, 196, 73]</td>\n",
       "      <td>[[[1356.1147, 1350.7999, 1363.1014, 1387.4152,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>[78, 202, 101]</td>\n",
       "      <td>[[[2210.428, 2129.2273, 1857.3464, 1581.8239, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>[78, 198, 106]</td>\n",
       "      <td>[[[1978.1129, 1896.9529, 1797.7844, 1599.226, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>[78, 197, 49]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 434.4572, 831...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>[78, 190, 116]</td>\n",
       "      <td>[[[1365.0511, 1352.2006, 1362.753, 1299.0201, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>[78, 139, 149]</td>\n",
       "      <td>[[[1787.9038, 1740.2826, 1708.1859, 1741.7373,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>[76, 204, 95]</td>\n",
       "      <td>[[[2183.7922, 2160.773, 2141.8716, 1927.3982, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>[76, 200, 51]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 358.82306, 1020.81036, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>[76, 178, 47]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 287.71655, 815.153...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>[76, 134, 151]</td>\n",
       "      <td>[[[1521.6403, 1470.4265, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>[75, 195, 108]</td>\n",
       "      <td>[[[2031.4017, 1927.355, 1812.0217, 1633.9812, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>[68, 194, 50]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 616.266, 1632...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>[67, 155, 24]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 307.4881, 657.96747, 993.834...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>[66, 205, 66]</td>\n",
       "      <td>[[[1135.9725, 1303.5363, 1550.3892, 1537.2714,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>[64, 123, 23]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.9...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>[62, 138, 50]</td>\n",
       "      <td>[[[2302.0, 2204.1243, 2161.6963, 2117.4167, 21...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>[60, 193, 98]</td>\n",
       "      <td>[[[1271.1519, 1036.4823, 826.4555, 588.8851, 3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>[56, 47, 50]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>[53, 187, 51]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 128.41635, 341.325...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>[53, 130, 55]</td>\n",
       "      <td>[[[2235.8909, 2260.4343, 2272.9822, 2287.9573,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>[52, 136, 57]</td>\n",
       "      <td>[[[2062.3188, 2081.6997, 2085.437, 2093.468, 2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>[50, 55, 51]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>[49, 180, 49]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 76....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>[49, 46, 62]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>[47, 165, 112]</td>\n",
       "      <td>[[[657.87665, 429.1054, 257.18488, 69.60442, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>[41, 113, 33]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 163...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>[37, 148, 66]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>[36, 64, 72]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>[35, 67, 87]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>[33, 97, 122]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>[27, 126, 83]</td>\n",
       "      <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>915 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            RST_peak                                        image_patch label  \\\n",
       "0     [171, 103, 87]  [[[1604.2019, 1612.2238, 1584.8153, 1550.3676,...   NaN   \n",
       "1      [171, 97, 61]  [[[1151.8575, 1269.8489, 1393.6145, 1520.887, ...   NaN   \n",
       "2     [170, 121, 58]  [[[1769.2383, 1805.1096, 1834.2352, 1837.5428,...   NaN   \n",
       "3     [170, 117, 71]  [[[1762.8202, 1846.0253, 1921.8511, 1976.3429,...   NaN   \n",
       "4     [170, 102, 53]  [[[710.26697, 800.9684, 878.76276, 935.9742, 9...   NaN   \n",
       "5      [170, 91, 84]  [[[1851.205, 1951.2527, 1994.4828, 1983.8191, ...   NaN   \n",
       "6     [169, 115, 97]  [[[1621.4022, 1678.7334, 1741.5708, 1791.8369,...   NaN   \n",
       "7    [168, 115, 105]  [[[1761.327, 1798.1428, 1875.3728, 1971.9221, ...   NaN   \n",
       "8     [168, 112, 88]  [[[1596.7714, 1608.4497, 1636.7616, 1668.0872,...   NaN   \n",
       "9      [168, 83, 73]  [[[2091.7402, 2410.6912, 2583.867, 2566.7422, ...   NaN   \n",
       "10    [167, 128, 97]  [[[1772.9629, 1727.7701, 1703.4581, 1691.2244,...   NaN   \n",
       "11     [167, 80, 85]  [[[1992.0345, 2051.3005, 2069.7288, 2123.867, ...   NaN   \n",
       "12    [166, 131, 69]  [[[1488.9651, 1445.1831, 1432.5006, 1435.0431,...   NaN   \n",
       "13    [166, 91, 112]  [[[1683.0985, 1633.1614, 1608.1573, 1595.7361,...   NaN   \n",
       "14     [165, 73, 68]  [[[733.609, 843.3025, 961.64136, 1040.1997, 10...   NaN   \n",
       "15    [164, 133, 43]  [[[2062.04, 2301.349, 2410.9116, 2411.8052, 23...   NaN   \n",
       "16    [161, 132, 40]  [[[1227.8506, 1212.9775, 1141.3005, 1080.7461,...   NaN   \n",
       "17    [156, 150, 64]  [[[1687.2054, 1651.17, 1615.944, 1622.3024, 16...   NaN   \n",
       "18    [153, 132, 25]  [[[0.0, 238.51714, 469.1484, 541.24243, 595.72...   NaN   \n",
       "19     [150, 52, 92]  [[[1644.8794, 1458.6715, 1234.8123, 1018.749, ...   NaN   \n",
       "20    [148, 159, 54]  [[[1227.709, 1333.4268, 1406.8408, 1442.3579, ...   NaN   \n",
       "21    [148, 133, 58]  [[[2012.3528, 1982.8635, 2000.4832, 2001.8976,...   NaN   \n",
       "22    [148, 53, 102]  [[[1427.9668, 1233.3207, 991.95715, 728.41437,...   NaN   \n",
       "23    [147, 182, 50]  [[[0.0, 0.0, 119.71727, 552.3697, 936.203, 114...   NaN   \n",
       "24    [146, 102, 40]  [[[302.26312, 190.05719, 170.38731, 469.99728,...   NaN   \n",
       "25     [146, 45, 73]  [[[705.09674, 721.1772, 756.4793, 769.5555, 74...   NaN   \n",
       "26    [145, 51, 106]  [[[725.55707, 475.2307, 319.07086, 248.6426, 1...   NaN   \n",
       "27    [144, 195, 61]  [[[1597.6909, 1786.7861, 1764.0107, 1727.4783,...   NaN   \n",
       "28    [144, 49, 102]  [[[793.2391, 650.29694, 499.59818, 400.52652, ...   NaN   \n",
       "29    [142, 196, 73]  [[[1356.1147, 1350.7999, 1363.1014, 1387.4152,...   NaN   \n",
       "..               ...                                                ...   ...   \n",
       "885   [78, 202, 101]  [[[2210.428, 2129.2273, 1857.3464, 1581.8239, ...   NaN   \n",
       "886   [78, 198, 106]  [[[1978.1129, 1896.9529, 1797.7844, 1599.226, ...   NaN   \n",
       "887    [78, 197, 49]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 434.4572, 831...   NaN   \n",
       "888   [78, 190, 116]  [[[1365.0511, 1352.2006, 1362.753, 1299.0201, ...   NaN   \n",
       "889   [78, 139, 149]  [[[1787.9038, 1740.2826, 1708.1859, 1741.7373,...   NaN   \n",
       "890    [76, 204, 95]  [[[2183.7922, 2160.773, 2141.8716, 1927.3982, ...   NaN   \n",
       "891    [76, 200, 51]  [[[0.0, 0.0, 0.0, 0.0, 358.82306, 1020.81036, ...   NaN   \n",
       "892    [76, 178, 47]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 287.71655, 815.153...   NaN   \n",
       "893   [76, 134, 151]  [[[1521.6403, 1470.4265, 0.0, 0.0, 0.0, 0.0, 0...   NaN   \n",
       "894   [75, 195, 108]  [[[2031.4017, 1927.355, 1812.0217, 1633.9812, ...   NaN   \n",
       "895    [68, 194, 50]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 616.266, 1632...   NaN   \n",
       "896    [67, 155, 24]  [[[0.0, 0.0, 0.0, 307.4881, 657.96747, 993.834...   NaN   \n",
       "897    [66, 205, 66]  [[[1135.9725, 1303.5363, 1550.3892, 1537.2714,...   NaN   \n",
       "898    [64, 123, 23]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.9...   NaN   \n",
       "899    [62, 138, 50]  [[[2302.0, 2204.1243, 2161.6963, 2117.4167, 21...   NaN   \n",
       "900    [60, 193, 98]  [[[1271.1519, 1036.4823, 826.4555, 588.8851, 3...   NaN   \n",
       "901     [56, 47, 50]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "902    [53, 187, 51]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 128.41635, 341.325...   NaN   \n",
       "903    [53, 130, 55]  [[[2235.8909, 2260.4343, 2272.9822, 2287.9573,...   NaN   \n",
       "904    [52, 136, 57]  [[[2062.3188, 2081.6997, 2085.437, 2093.468, 2...   NaN   \n",
       "905     [50, 55, 51]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "906    [49, 180, 49]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 76....   NaN   \n",
       "907     [49, 46, 62]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "908   [47, 165, 112]  [[[657.87665, 429.1054, 257.18488, 69.60442, 0...   NaN   \n",
       "909    [41, 113, 33]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 163...   NaN   \n",
       "910    [37, 148, 66]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "911     [36, 64, 72]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "912     [35, 67, 87]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "913    [33, 97, 122]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "914    [27, 126, 83]  [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...   NaN   \n",
       "\n",
       "    patient_name  \n",
       "0             12  \n",
       "1             12  \n",
       "2             12  \n",
       "3             12  \n",
       "4             12  \n",
       "5             12  \n",
       "6             12  \n",
       "7             12  \n",
       "8             12  \n",
       "9             12  \n",
       "10            12  \n",
       "11            12  \n",
       "12            12  \n",
       "13            12  \n",
       "14            12  \n",
       "15            12  \n",
       "16            12  \n",
       "17            12  \n",
       "18            12  \n",
       "19            12  \n",
       "20            12  \n",
       "21            12  \n",
       "22            12  \n",
       "23            12  \n",
       "24            12  \n",
       "25            12  \n",
       "26            12  \n",
       "27            12  \n",
       "28            12  \n",
       "29            12  \n",
       "..           ...  \n",
       "885           13  \n",
       "886           13  \n",
       "887           13  \n",
       "888           13  \n",
       "889           13  \n",
       "890           13  \n",
       "891           13  \n",
       "892           13  \n",
       "893           13  \n",
       "894           13  \n",
       "895           13  \n",
       "896           13  \n",
       "897           13  \n",
       "898           13  \n",
       "899           13  \n",
       "900           13  \n",
       "901           13  \n",
       "902           13  \n",
       "903           13  \n",
       "904           13  \n",
       "905           13  \n",
       "906           13  \n",
       "907           13  \n",
       "908           13  \n",
       "909           13  \n",
       "910           13  \n",
       "911           13  \n",
       "912           13  \n",
       "913           13  \n",
       "914           13  \n",
       "\n",
       "[915 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>RST_peak</th>\n",
       "      <th>image_patch</th>\n",
       "      <th>label</th>\n",
       "      <th>patient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>[139, 142, 67]</td>\n",
       "      <td>[[[2553.3855, 2612.0293, 2648.8555, 2673.9736,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>[94, 167, 119]</td>\n",
       "      <td>[[[1699.5485, 1655.255, 1731.7466, 1842.4784, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RST_peak                                        image_patch label  \\\n",
       "766  [139, 142, 67]  [[[2553.3855, 2612.0293, 2648.8555, 2673.9736,...   NaN   \n",
       "767  [94, 167, 119]  [[[1699.5485, 1655.255, 1731.7466, 1842.4784, ...   NaN   \n",
       "\n",
       "    patient_name  \n",
       "766            5  \n",
       "767            5  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data.loc[total_data['patient_name'] == '5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QCAMMEmGAQSIMMEiEAQaJMMAgEQYY9L/Ovr3I4iJ/xwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgplot(total_data.iloc[766]['image_patch'], slice_no=[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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