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Towards-Unified-Surgical-Skill-Assessment

Codes for Towards Unified Surgical Skill Assessment (CVPR 2021).

Project Page

Setup

• Recommended Environment: Python 3.7, Cuda 10.1, PyTorch 1.6.0
• Install dependencies: pip3 install -r requirements.txt.

Data

1. Complete the access form of the JIGSAWS dataset and get the permission.
2. Download our processed data for JIGSAWS from Baidu Yun (PIN:sa67) or Google Drive.
3. Unzip the files by zip --fix data.zip --out data_full.zip && unzip data_full.zip.
4. Put the data into the parent directory of the codes.
5. The data includes following sub-directories:

video_encoded : Surgical videos after pre-processing.

label : Ground truth scores of surgical skills.

feature_resnet101 : ImageNet-pretrained ResNet features with ten-crop augmentation (Visual Path Input).

kinematics_GT_14_1 : Kinematic data of the robotic surgical instruments (Tool Path Input).

time_val_1 : The sequences indicating task completion time (Proxy Path Input).

gesture_prediction : Surgical event preditions from MS-TCN models (Event Path Input).

As for the clinical dataset used in the paper, it might be released later if approved.

Run

Simply run python3 main.py --config some_config_file.json .

The config files for our full model under the JIGSAWS 4-fold cross-validation setting are provided in the configs folder.

Trained models and Tensorboard logs will be saved in the result folder.

Our trained models and logs are provided in the pre_result folder.

Citation

@inproceedings{liu2021towards, title={Towards Unified Surgical Skill Assessment}, author={Liu, Daochang and Li, Qiyue and Jiang, Tingting and Wang, Yizhou and Miao, Rulin and Shan, Fei and Li, Ziyu}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={9522--9531}, year={2021} }

License

MIT