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This repository contains the code for the paper High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

• Python 3.8
• Pytorch 1.8.1
• monai 0.9.0rc2+2.gbbc628d9 (results in paper submitted to hindawi)
• monai 0.9.1 (new results)
• nnunet pip install nnunet
• pip install axial-attention==0.5.0
• pip install mmcv-full
• pip install einops
• pip install SimpleITK
• pip install tensorboardX
• pip install omegaconf
• pip install fvcore

• Ubuntu 18.04
• cuda 10.2
• cudnn 8.1.1

• Modify the parameters about datasets path in the configs/dataset_path.yaml file.

1. The config file for training BraTS 2021 dataset is configs/hr_trans_brats_2021_seg.yaml.
2. The following parameters in the above config file should be specified before training:
   • MODEL.HR_TRANS.STAGE_NUM: the number of stages in the HRSTNet.
   • DATALOADER.TRAIN_WORKERS: the number of workers of pytorch dataloader for training. This parameter should be set according to your computer configuration.
   • SOLVER.EPOCHS: training epochs.
   • OUTPUT_DIR: the path to save the training log and model files.
   • INPUT.RAND_CROP.SAMPLES: the number of random crop samples in each batch, and the real batch size equals INPUT.RAND_CROP.SAMPLES*SOLVER.BATCH_SIZE.
3. Using the file tools_train.py to train the model.
4. Modify the following parameters i the tools_train.py file before training:
   • --config_file: the path to the config file that is used to train model.
   • --num-gpus: gpu count used to train model.

1. The config file for training MSD dataset is configs/hr_trans_liver_seg.yaml.
2. The following parameters in the above config file should be specified before training:
   • MODEL.HR_TRANS.STAGE_NUM: the number of stages in the HRSTNet.
   • DATALOADER.TRAIN_WORKERS: the number of workers of pytorch dataloader for training. This parameter should be set according to your computer configuration.
   • SOLVER.EPOCHS: training epochs.
   • OUTPUT_DIR: the path to save the training log and model files.
   • INPUT.RAND_CROP.SAMPLES: the number of random crop samples in each batch, and the real batch size equals INPUT.RAND_CROP.SAMPLES*SOLVER.BATCH_SIZE.
3. Using the file tools_train.py to train the model.
4. Modify the following parameters i the tools_train.py file before training:
   • --config_file: the path to the config file that is used to train model.
   • --num-gpus: gpu count used to train model.

1. The config file for training MSD dataset is configs/hr_trans_abdomen_seg.yaml.
2. The following parameters in the above config file should be specified before training:
   • MODEL.HR_TRANS.STAGE_NUM: the number of stages in the HRSTNet.
   • DATALOADER.TRAIN_WORKERS: the number of workers of pytorch dataloader for training. This parameter should be set according to your computer configuration.
   • SOLVER.EPOCHS: training epochs.
   • OUTPUT_DIR: the path to save the training log and model files.
   • INPUT.RAND_CROP.SAMPLES: the number of random crop samples in each batch, and the real batch size equals INPUT.RAND_CROP.SAMPLES*SOLVER.BATCH_SIZE.
3. Using the file tools_train.py to train the model.
4. Modify the following parameters i the tools_train.py file before training:
   • --config_file: the path to the config file that is used to train model.
   • --num-gpus: gpu count used to train model.

After training the models by using the tools_train.py file, the performance of models are evaluated by using the tools_inference.py file. If running the evaluate_*.py encounter the following error: ITK ERROR: ITK only supports orthonormal direction cosines. No orthonormal definition found! Running the code evaluate/fix_simpleitk_read_error.py file to fix the wrong file, and setting the parameter pred_file_path to the wrong file path, and the original_img_path to the corresponding image path.

If the model is trained by using the vt_unet preprocessing method, the model is evaluated by the following method (taking hrstnet as example).

1. modify the parameters MODEL.WEIGHTS, OUTPUT_DIR, and MODE in the configs/hr_trans_brats_2021_seg.yaml.
2. set the parameter space=brats_2021_vt_unet, and config_file=confgs/configs/hr_trans_brats_2021_seg.yaml in the tools_inference.py file.
3. Running the tools_inference.py file, and the segmentation results will be generated in the folder OUTPUT_DIR/seg_results.
4. The segmentation masks in folder OUTPUT_DIR/seg_results can be used to visualize in the 3D Slicer software.
5. Running the code evaluate/evaluate_brats_vt_unet.py, and setting the parameters inferts_path, and path to OUTPUT_DIR/seg_results, and ground_truth_path, respectively.
6. The evaluation results will appear in OUTPUT_DIR/seg_results/dice_pre.txt.

Taking the Spleen dataset from MSD as an example.

1. modify the parameters MODEL.WEIGHTS, OUTPUT_DIR, and MODE in the configs/hr_spleen_seg.yaml.
2. set the parameter space=original_msd, and config_file=confgs/configs/hr_trans_spleen_seg.yaml in the tools_inference.py file.
3. Running the tools_inference.py file, and the segmentation results will be generated in the folder OUTPUT_DIR/seg_results/.
4. The segmentation masks in folder OUTPUT_DIR/seg_results can be used to visualize in the 3D Slicer software.
5. Running the code evaluate/evaluate_msd.py, and setting the parameters pred_path, MSD_TYPE, CATEGORIES, MODE and gt_path to OUTPUT_DIR/seg_results, Spleen, 2, VALIDATE and ground_truth_path, respectively.
6. The evaluation results will appear in OUTPUT_DIR/seg_results/dice_pred.txt.
7. Running the code evaluate/evaluate_msd.py, and setting the parameter MODE to VAL to check does all the generated files are correct.

1. modify the parameters MODEL.WEIGHTS, OUTPUT_DIR, DATASETS.TEST_TYPE, and MODE in the configs/hr_trans_abdomen_seg.yaml.
2. set the parameter space=original_abdomen, and config_file=confgs/configs/hr_trans_abdomen_seg.yaml in the tools_inference.py file.
3. Running the tools_inference.py file, and the segmentation results will be generated in the folder OUTPUT_DIR/seg_results/.
4. Utilizing the evaluate/fix_aliginment_error.py file to modify the origin and direction of saved CT segmentation file, otherwise the segmentation mask can not display correctly. Changing the pred_img_folder, and the gt_img_folder parameters.

pred_img_folder = "/home/ljm/Fdisk_A/train_outputs/train_output_medical_2022_8/hrstnet/abdomen_seg_hrstnet_stages_4/seg_results/"
gt_img_folder = "/home/ljm/Fdisk_A/train_datasets/train_datasets_medical/2015_Segmentation_Cranial Vault Challenge/Abdomen/RawData/Training/img/"

5. The segmentation masks in folder OUTPUT_DIR/seg_results can be used to visualize in the 3D Slicer software.
6. Running the code evaluate/evaluate_abdomen.py, and setting the parameters pred_path, and gt_path to OUTPUT_DIR/seg_results, Abdomen/RawData/Training/label/, respectively.
7. The evaluation results will appear in OUTPUT_DIR/seg_results/dice_pred.txt.

• tools_visualize.py: is used to visualize the segmentation results.
• tools_flops.py: is used to count the flops of models.

1. detectron2
2. MONAI
3. UNETR
4. VT-UNET
5. nnUnet
6. Extending nn-UNet
7. Optimized UNet

If you find this project useful for your research, please cite our paper:

@Article{s23073420,
    AUTHOR = {Wei, Chen and Ren, Shenghan and Guo, Kaitai and Hu, Haihong and Liang, Jimin},
    TITLE = {High-Resolution Swin Transformer for Automatic Medical Image Segmentation},
    JOURNAL = {Sensors},
    VOLUME = {23},
    YEAR = {2023},
    NUMBER = {7},
    ARTICLE-NUMBER = {3420},
    URL = {https://www.mdpi.com/1424-8220/23/7/3420},
    ISSN = {1424-8220},
    DOI = {10.3390/s23073420}
}

Chen Wei

email: [email protected]