SC-Net

This is the official code for our MedIA paper:

Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

Highlights

In this work, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. The proposed method achieves label propagation in a coarse-to-fine manner as follows. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. [comment]: <> () 

### Using the code Please clone the following repositories: ``` git clone https://github.com/hust-linyi/SC-Net.git ``` ### Requirement ``` pip install -r requirements.txt ``` ### Data preparation #### Download 1. **MoNuSeg** [Multi-Organ Nuclei Segmentation dataset](https://monuseg.grand-challenge.org) 2. **CPM** [Computational Precision Medicine dataset](https://drive.google.com/drive/folders/1sJ4nmkif6j4s2FOGj8j6i_Ye7z9w0TfA) #### Pre-processing Please refer to [dataloaders/prepare_data.py](https://github.com/hust-linyi/SC-Net/blob/main/dataloaders/prepare_data.py) for the pre-processing of the datasets. ### Training 1. Configure your own parameters in [opinions.py](https://github.com/hust-linyi/SC-Net/blob/main/options.py), including the dataset path, the number of GPUs, the number of epochs, the batch size, the learning rate, etc. 2. Run the following command to train the model: ``` python train.py ``` ### Testing Run the following command to test the model: ``` python test.py ``` ## Citation Please cite the paper if you use the code. ```bibtex @article{lin2023nuclei, title={Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training}, author={Lin, Yi and Qu, Zhiyong and Chen, Hao and Gao, Zhongke and Li, Yuexiang and Xia, Lili and Ma, Kai and Zheng, Yefeng and Cheng, Kwang-Ting}, journal={Medical Image Analysis}, pages={102933}, year={2023}, publisher={Elsevier} } ```