This code is for the virtual staining part of paper Resection-Inspired Histopathological Diagnosis of Cerebral Cavernous Malformations Using Quantitative Multiphoton Microscopy.

• 2023.09.15 🔥 The related work of CCM-StainGAN has been published by Nature Communications (paper).
• 2022.11.02 🎉 The article was selected as the cover of the journal.
• 2022.08.30 🚀 Update the code of the repo.

2 Channels	2 Channels	3 Channels

• Python 3.6 (Recommend to use Anaconda)

• NVIDIA GPU + CUDA

• PyTorch >= 1.8.0

• Python packages:

  cd CCM-stainGAN
  
  conda activate CCM-stainGAN
  
  pip install -r requirements.txt

• The MPM and H&E images are available, download from (Google Drive / Baidu Disk). Other data used in this repo are available from the corresponding author upon reasonable request.

  Note: access code for Baidu Disk is ccm1.

• Split the images into patches.

• Place the training MPM patches to /CCM-stainGAN/dataset/trian/A/ and the H&E patches to /CCM-stainGAN/dataset/trian/B/.

• Place the training MPM category patches to /CCM-stainGAN/dataset/trian/cls_A and the H&E category patches to /CCM-stainGAN/dataset/trian/cls_B .

• Place the testing MPM patches to /CCM-stainGAN/dataset/test/A/ and the H&E patches to /CCM-stainGAN/dataset/test/B/.

• Then this repo is collected as the following form:

  |--CCM-stainGAN
    |--train.py
    |--utils.py
    |--data_utils.py
    |--test.py
    |--output
    |--dataset
      |--MPM2HE
        |--train
          |--A
            |--mpm_000.png
            |--mpm_001.png
            :
            |--mpm_099.png
          |--B
            |--he_000.png
            |--he_001.png
            :
            |--he_099.png
          |--cls_A
            |--he_001_0.png
            |--he_002_0.png
            :
            |--he_099_8.png
          |--cls_B
            |--mpm_001_0.png
            |--mpm_002_0.png
            :
            |--mpm_099_8.png
        |--test
          |--A
            |--mpm_000.png
            :
            |--mpm_099.png
          |--B
            |--he_000.png
            :
            |--he_099.png

  Note:

  The image's name of category dataset must add category label in the end of name , which split of '_'. Such as: img_001_0.png, img_002_0.png, img_003_1.png, img_004_1.png. The last number is the label of the dataset.

(1) Download the data and split the images in the correct place.

(2) Run the following command to train the model.

• Single GPU

  cd /CCM-stainGAN/
  
  # training by CCM-stainGAN
  python train.py --gpu_ids 0 --end_epoch 100 --batch_size 4 --batch_size_cls 16 --num_classes 9 --decay_epoch 50 --threshold_A 35 --threshold_B 180 --env ccm
  
  # training by CycleGAN
  python train_cycle.py --gpu_ids 0 --end_epoch 100 --batch_size 4 --decay_epoch 50 --env cycle
  
  # training by UTOM
  python train_utom.py --gpu_ids 0 --end_epoch 100 --batch_size 4 --decay_epoch 50 --threshold_A 35 --threshold_B 180 --env utom

• Multi GPUs

  Please change the gpu_ids to adapt your device.

  cd /CCM-stainGAN/
  
  # training by CCM-stainGAN
  python train.py --gpu_ids 0,1,2,3 --end_epoch 100 --batch_size 4 --batch_size_cls 16 --num_classes 9 --decay_epoch 50 --threshold_A 35 --threshold_B 180 --env ccm
  
  # training by CycleGAN
  python train_cycle.py --gpu_ids 0,1,2,3 --end_epoch 100 --batch_size 4 --decay_epoch 50 --env cycle
  
  # training by UTOM
  python train_utom.py --gpu_ids 0,1,2,3 --end_epoch 100 --batch_size 4 --decay_epoch 50 --threshold_A 35 --threshold_B 180 --env utom

• Multi GPUs With DDP

  We also provide the DistributedDataParallel version of multi GPUs script. Please change the gpu_ids to adapt your device.

  cd /CCM-stainGAN/
  
  # training by CCM-stainGAN
  python -m torch.distributed.launch --nproc_per_node=6 --master_port 11111 train_ddp.py --gpu_ids 0,1,2,3,4,5 --end_epoch 100 --batch_size 4 --batch_size_cls 8 --num_classes 9 --decay_epoch 50 --threshold_A 35 --threshold_B 180 --env ccm

The models will be saved in /CCM-stainGAN/output/{env}/.

• Run the following command to test the result.

• Please change the pretrained_model_path and gpu_ids to adapt your model and device.

  cd /CCM-stainGAN/
  
  # transform by CCM-stainGAN
  python test.py --gpu_ids 0 --pretrained_model_path epoch100.pth --outf ./output/test 

If the repo helps you, please consider citing our works:

@article{wang2022resection,
  title={Resection-inspired histopathological diagnosis of cerebral cavernous malformations using quantitative multiphoton microscopy},
  author={Wang, Shu and Li, Yueying and Xu, Yixuan and Song, Shiwei and Lin, Ruolan and Xu, Shuoyu and Huang, Xingxin and Zheng, Limei and Hu, Chengcong and Sun, Xinquan and others},
  journal={Theranostics},
  volume={12},
  number={15},
  pages={6595},
  year={2022},
  publisher={Ivyspring International Publisher}
}

@article{wang2023deep,
  title={A deep learning-based stripe self-correction method for stitched microscopic images},
  author={Wang, Shu and Liu, Xiaoxiang and Li, Yueying and Sun, Xinquan and Li, Qi and She, Yinhua and Xu, Yixuan and Huang, Xingxin and Lin, Ruolan and Kang, Deyong and others},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={5393},
  year={2023},
  publisher={Nature Publishing Group UK London}
}