DoDNet

<p align="left"> <img src="a_DynConv/dodnet.png" width="85%" height="85%"> </p>

This repo holds the pytorch implementation of DoDNet and TransDoDNet:<br />

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets (https://arxiv.org/pdf/2011.10217.pdf)
Learning from partially labeled data for multi-organ and tumor segmentation (https://arxiv.org/pdf/2211.06894.pdf)

<!-- ## Requirements Python 3.7<br /> PyTorch==1.4.0<br /> <<<<<<< HEAD [Apex==0.1](https://github.com/NVIDIA/apex)<br /> [batchgenerators](https://github.com/MIC-DKFZ/batchgenerators)<br /> ======= Apex==0.1<br /> -->

Usage

<!--### 0. Installation * Clone this repo ``` git clone https://github.com/jianpengz/DoDNet.git ``` -->

1. MOTS Dataset Preparation

Before starting, MOTS should be re-built from the serveral medical organ and tumor segmentation datasets

Partial-label task | Data source --- | :---: Liver | data Kidney | data Hepatic Vessel | data Pancreas | data Colon | data Lung | data Spleen | data

<!-- * Download and put these datasets in `dataset/0123456/`. * Re-spacing the data by `python re_spacing.py`, the re-spaced data will be saved in `0123456_spacing_same/`. The folder structure of dataset should be like dataset/0123456_spacing_same/ ├── 0Liver | └── imagesTr | ├── liver_0.nii.gz | ├── liver_1.nii.gz | ├── ... | └── labelsTr | ├── liver_0.nii.gz | ├── liver_1.nii.gz | ├── ... ├── 1Kidney ├── ... -->

• Preprocessed data will be available soon.

2. Training/Testing/Evaluation

sh run_script.sh

<!-- ### 2. Model Pretrained model is available in [checkpoint](https://drive.google.com/file/d/1qj8dJ_G1sHiCmJx_IQjACQhjUQnb4flg/view?usp=sharing) ### 3. Training * cd `a_DynConv/' and run ``` CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=$RANDOM train.py \ --train_list='list/MOTS/MOTS_train.txt' \ --snapshot_dir='snapshots/dodnet' \ --input_size='64,192,192' \ --batch_size=2 \ --num_gpus=2 \ --num_epochs=1000 \ --start_epoch=0 \ --learning_rate=1e-2 \ --num_classes=2 \ --num_workers=8 \ --weight_std=True \ --random_mirror=True \ --random_scale=True \ --FP16=False ``` ### 4. Evaluation ``` CUDA_VISIBLE_DEVICES=0 python evaluate.py \ --val_list='list/MOTS/MOTS_test.txt' \ --reload_from_checkpoint=True \ --reload_path='snapshots/dodnet/MOTS_DynConv_checkpoint.pth' \ --save_path='outputs/' \ --input_size='64,192,192' \ --batch_size=1 \ --num_gpus=1 \ --num_workers=2 ``` ### 5. Post-processing ``` python postp.py --img_folder_path='outputs/dodnet/' ``` -->

3. Citation

If this code is helpful for your study, please cite:

@inproceedings{zhang2021dodnet,
  title={DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets},
  author={Zhang, Jianpeng and Xie, Yutong and Xia, Yong and Shen, Chunhua},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={},
  year={2021}
}
@article{xie2023learning,
  title={Learning from partially labeled data for multi-organ and tumor segmentation},
  author={Xie, Yutong and Zhang, Jianpeng and Xia, Yong and Shen, Chunhua},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023}
}