Skip to content

Latest commit

 

History

History
105 lines (68 loc) · 4.5 KB

README.md

File metadata and controls

105 lines (68 loc) · 4.5 KB

Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

by Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero and Carola-Bibiane Schonlieb.

Introduction

In this repository, we provide Pytorch implementation for the 4th solution in the grand challenge CoNIC-2022. The detailed description can be found in our MIUA-22 paper Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting.

Requirement

pytorch 1.10.1
cuda 11.0
cudnn 8.0

To install detectron2, you need to install the it from my repo, since I make changes to the orginl detectron2:

cd detectron2
python setup.py install

Code Structure

There are three parts (three subdirs) in this repo, which are exactly the same three parts listed in the main paper:

  1. The instance model in subdir detectron2, which contrains the training and testing logic of the Cascade Mask-RCNN 152 model forked and modified from the original detectron2.

  2. The semantic model in subdir hover_net, which contrains the training and testing logic of the Hover-Net model.

  3. The subdir conic, which contrains the data pre-processing and NMS embedding modole.

Usage

  1. Download the dataset from the challenge website: https://conic-challenge.grand-challenge.org/, and unzip everything in folder ./conic/dataset.

  2. Run the three pre-processing files in ./conic/utils/ in order,

    cd script
python 1_generate_5fold_split.py
python 2_turn_npy_to_png.py
python 3_generate_coco_format_data.py

    The generated input files for detectron2 and hovernet are stored in ./conic/dataset/detectron2 and ./conic/dataset/hovernet, respectively.

  3. Run the instance model in subdir detectron2:

    cd detectron2
./tools/train_net.py --num-gpus 4 --config-file ./configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml

    Note: you need to change the batch_size, training steps parameters accordingly in line 29-36 in ./detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml before training. I used 4 A100 GPUs with 80 GB memory. My batch size is 96 and max step is 3200. If you decrease the batch size, please increase the max iteration, checkpoint period, and so on. For instance,

      IMS_PER_BATCH: 8        # 8 = 96 / 12
  STEPS: (28800, 22600)   # (1800 * 12, 2200 * 12)
  MAX_ITER: 38400         # 3200 * 12
  BASE_LR: 0.04
  WARMUP_ITERS: 2400      # 200 * 12
  WARMUP_FACTOR: 0.01
  CHECKPOINT_PERIOD: 2400 # 200 * 12

  4. Run the semantic model in subdir hover_net:

    cd hovernet
python run.py --gpu '0,1,2,3' --fold 0

    Note: you need to change the batch_size, nr_procs, and pretrained_backbon parameters in ./hover_net/param/template.yaml before training

  5. Run the NMS code after on the predicted results on 3 & 4:

    cd conic
python ensemble_semantic_and_instance_prediction_with_nms.py --instance_pred your_instance_test_result.npy --semantic_pred your_instance_test_result.npy --out_dir_path your_output_dir --fold 0

Citation

If you use our code for your research, please cite our paper:

@article{liu2022simultaneous,
  title={Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting},
  author={Liu, Lihao and Hong, Chenyang and Aviles-Rivero, Angelica I and Sch{\"o}nlieb, Carola-Bibiane},
  journal={arXiv preprint arXiv:2203.00157},
  year={2022}
}

Question

Please open an issue or email [email protected] for any questions.