Awesome
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)
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 |
- 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}
}