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This is modified repository of HoVer-Net.

HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images

A multiple branch network that performs nuclear instance segmentation and classification within a single network. The network leverages the horizontal and vertical distances of nuclear pixels to their centres of mass to separate clustered cells. A dedicated up-sampling branch is used to classify the nuclear type for each segmented instance.

Our paper:

A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

Original HoVer-Net papers:

Link to Medical Image Analysis paper

Link to arxiv

Set Up Environment using Docker

Use the following repository to set up environment:

hover_build

Getting Started

Inside docker container

Preparation:

  1. Edit generate.sh
  2. Run generate.sh for creating config.yml
  3. Consider running misc/proc_consep_ann.py and misc/proc_pannuke_ann.py once for dataset label preparation

Overall pipeline consits of running scripts consecutively.

  1. (optional) stain_norm.py - Normalize dataset
  2. extract_patches.py - Extract smaller patches for training
  3. train.py - Train the model
  4. infer.py - Perform inference
  5. process.py - Perform post-processing
  6. (optional) compute_stats.py - Evaluate results
  7. (optional) export_model.py - Export model as (.pb) and as checkpoint

Repository Structure

  • misc/info.py contains predefined variables, specific to dataset.
  • config.py is the configuration file.
  • src/ contains executable files used to run the model.
  • loader/contains scripts for data loading and self implemented augmentation functions.
  • metrics/contains evaluation code.
  • misc/contains util and data preparation scripts.
  • model/ contains scripts that define the architecture of the segmentation models.
  • opt/ contains scripts that define the model hyperparameters.
  • postproc/ contains post processing utils.
  • metrics/counts.py is the file we used for counting TILs and Cancer cells for patients.

Citation

If any part of this code is used, please give appropriate citation to original authors paper.

BibTex entry: 

@article{graham2019hover,
  title={Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images},
  author={Graham, Simon and Vu, Quoc Dang and Raza, Shan E Ahmed and Azam, Ayesha and Tsang, Yee Wah and Kwak, Jin Tae and Rajpoot, Nasir},
  journal={Medical Image Analysis},
  pages={101563},
  year={2019},
  publisher={Elsevier}
}

Original authors

License

This project is licensed under the MIT License - see the LICENSE file for details