This is the official PyTorch implementation of SENUCLS, a graph neural network based method for nuclei classification. The framework consists of a pixel-wise feature extraction branch (upper) and an instance-level classification branch (lower) using the inter-nucleus Graph Structure Learning module (GSL).

In the GSL module, an intra-nucleus Polygon Structure Learning module (PSL) computes the shape features of nuclei. Then the input image is transformed into a graph and a GNN enhances the features of the graph nodes for nuclei classification.

Part of the codes are from the implementation of Hover-Net.

If you intend to use anything from this repo, citation of the original publication given above is necessary

Set Up Environment

conda env create -f environment.yml
conda activate hovernet
pip install torch==1.10.0 torchvision==0.11.1
pip install torch-geometric torch-scatter torch-sparse

Datasets

• CoNSeP
• PanNuke
• MoNuSAC

Running the Code

Training

Data Format

For training, patches must be extracted using extract_patches.py. For each patch, patches are stored as a 4 dimensional numpy array with channels [RGB, inst]. Here, inst is the instance segmentation ground truth. I.e pixels range from 0 to N, where 0 is background and N is the number of nuclear instances for that particular image.

Before training:

• Set path to the data directories in config.py

• Set path where checkpoints will be saved in config.py

• Set path to pretrained VAN-base weights in models/senucls/opt.py. Download the weights here.

• Modify hyperparameters, including number of epochs and learning rate in models/senucls/opt.py.

• Set edge number, point number and class weights for Focal loss in models/senucls/run_desc.PY.

• To initialise the training script with GPUs 0, the command is:

python run_train.py --gpu='0' 

Inference

Data Format

Input:

• Standard images files, including png, jpg and tiff.
• WSIs supported by OpenSlide, including svs, tif, ndpi and mrxs.
• Instance segmentation results output from other methods, like HoverNet or MaskRCNN. The formats of the segmentation results are '.mat'. The filename should match the testing images.

Inference codes for tiles

python -u run_infer.py \
--gpu='0' \
--nr_types=6 \ # number of types + 1
--type_info_path=type_info.json \
--batch_size=1 \
--model_mode=original \
--model_path=.tar \ # choose the trained weights
--nr_inference_workers=1 \
--nr_post_proc_workers=16 \
tile \
--input_dir='PaNuKe/Fold3/images/' \ # testing tile path
--output_dir=panuke_out/ \  # output path
--inst_dir='inst_prediction/' \ # instance segmentation results path
--mem_usage=0.1 \
--save_qupath

Output: :

• mat files / JSON files : Including centroid coordinates and nuclei types.
• overlay images: Visualization of the classification results.

Inference codes for WSI

python run_infer.py \
--gpu='0' \
--nr_types=6 \ # number of types + 1
--type_info_path=type_info.json \
--batch_size=1 \
--model_mode=original \
--model_path=.tar \ # choose the trained weights
--nr_inference_workers=1 \
--nr_post_proc_workers=0 \
wsi \
--input_dir='test/wsi/' \ # testing wsi path
--output_dir='wsi_out/' \ # output path
--inst_pred_dir='test/inst_pred/' \ # instance segmentation results path
--proc_mag=20 \
--input_mask_dir='test/msk/' \
--save_thumb \
--save_mask

Output: :

• JSON files : Including centroid coordinates and nuclei types.

Post process to .svs file:

python prediction2svs.py # change input file name in the codes

Evaluation:

To calculate the metrics used in this paper, run the command:

• type classification: python compute_stats.py --mode=type --pred_dir='pred_dir' --true_dir='true_dir'

Citation

If any part of this code is used, please give appropriate citations to our paper.

BibTex entry:

@article{lou2024structure,
  title={Structure embedded nucleus classification for histopathology images},
  author={Lou, Wei and Wan, Xiang and Li, Guanbin and Lou, Xiaoying and Li, Chenghang and Gao, Feng and Li, Haofeng},
  journal={IEEE Transactions on Medical Imaging},
  year={2024},
  publisher={IEEE}
}