SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions

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)

Installation

We provided docker for easy installation, the environment can be easily set up via:

cd docker
docker build ./ -t segstrongc:latest
docker run --rm -v "LOCAL_DATADIR":/workspace/data --gpus='"device={GPU_IDS}"' -it segstrongc:latest

Usage

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

for example:

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

for example:

python validate.py --config UNet_SegSTRONGC --model_path checkpoints/unet_segstrongc/model_39.pth --domain regular

The final test will be on test set(for example):

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

Dataset preparation:

Please refer to our (website) for registration and data downloading

Citations

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},
}

License

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）