This repo hosts the code for implementing the baseline algorithms for SegSTRONG-C.
This challenge is originated from:
CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data,
Hao Ding, Jintan Zhang, Peter Kazanzides, Jie Ying Wu, Mathias Unberath Proc. MICCAI, 2022
arXiv preprint (arXiv 2203.09475)Rethinking Causality-driven Robot Tool Segmentation with Temporal Constraints, Hao Ding, Jie Ying Wu, Zhaoshuo Li, Mathias Unberath Int J CARS 18, 1009–1016 (2023) arXiv preprint (arXiv 2203.09475)
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge, Hao Ding, Tuxun Lu, Yuqian Zhang, Ruixing Liang, Hongchao Shu, Lalithkumar Seenivasan, Yonghao Long, Qi Dou, Cong Gao, Mathias Unberath 2024 arXiv preprint (arXiv 2203.09475)
cd docker
docker build ./ -t segstrongc:latest
docker run --rm -v "LOCAL_DATADIR":/workspace/data --gpus='"device={GPU_IDS}"' -it segstrongc:latest
We only used one GPU for training and inference so we haven't implement multi-gpu version.
To run training, find the right name for the config you want in the file:
python train.py --config CONFIG_FILENAME
python train.py --config UNet_SegSTRONGC
To run inference on validation set, give the name of the config and the path to the checkpoint file for networks to load:
python validate.py --config CONFIG_FILENAME --model_path CHECKPOINT_PATH --domain DOMAIN_NAME
python validate.py --config UNet_SegSTRONGC --model_path checkpoints/unet_segstrongc/model_39.pth --domain regular
python validate.py --config UNet_SegSTRONGC --model_path checkpoints/unet_segstrongc/model_39.pth --test True --domain smoke --save_dir /workspace/data/SegSTRONG-C/results/smoke
Please refer to our (website) for registration and data downloading
Please consider citing our papers in your publications if this repo helps you.
@inproceedings{ding2022carts,
title={CaRTS: Causality-Driven Robot Tool Segmentation from Vision and Kinematics Data},
author={Ding, Hao and Zhang, Jintan and Kazanzides, Peter and Wu, Jie Ying and Unberath, Mathias},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={387--398},
year={2022},
organization={Springer}
}
@article{Ding2022RethinkingCR,
title={Rethinking causality-driven robot tool segmentation with temporal constraints},
author={Hao Ding and Jie Ying Wu and Zhaoshuo Li and M. Unberath},
journal={International Journal of Computer Assisted Radiology and Surgery},
year={2022},
pages={1009 - 1016},
}
@misc{ding2024segstrongcsegmentingsurgicaltools,
title={SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge},
author={Hao Ding and Tuxun Lu and Yuqian Zhang and Ruixing Liang and Hongchao Shu and Lalithkumar Seenivasan and Yonghao Long and Qi Dou and Cong Gao and Mathias Unberath},
year={2024},
eprint={2407.11906},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.11906},
}
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Hao Ding (email) and Mathias Unberath(email）