This repo holds the pytorch implementation of DoDNet and TransDoDNet:
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)
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.
sh run_script.sh
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}
}