RL-Surgical-Gesture-Segmentation

This is the code of Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification(Supplementary)

(MICCAI 2018)

This repo includes two parts:

1. Re-implementation of Temporal Convolutional Networks (For the original version, please refer to Colin Lea's repo).
2. The MDP formulation and a customized OpenAI Gym Env. Policy training is implemented with OpenAI Baseline.

Install

1. Install scipy, numpy, pandas, matplotlib
2. Install pytorch, tensorflow, gym
3. Clone this snapshot version of OpenAI Baseline and follow its install instructions. The offical library is under rapid development and often brings breaking changes
4. Clone this repo

Dataset

The code is tested on the three tasks of JIGSAWS dataset (Video and sensor data for the suturing. Only sensor data for the knot-tying and the needle-passing).

We use the same data features and splits as Colin Lea. Request for the data and split files from Colin and put them into folder ./raw_features and ./splits.

We also test on other datasets such as 50Salads and GTEA but do not formally benchmark or claim any results.

Please be careful about the naming of JIGSAWS:

'JIGSAWS': The suturing task in JIGSAWS

'JIGSAWS_K': The knot tying task in JIGSAWS

'JIGSAWS_N': The needle passing task in JIGSAWS

How to Run

Hyper-parameters

Setttings and hyper-parameters are in config.json. The defaults work well but you can modify it.

Run the whole experiment (Recommanded)

First setup the experiment in experiment.py. Then:

cd <this repo>

python3 experiment.py

python3 export_csv.py

The results will be in CSV files. It takes about 24h for one dataset.

Run TCN alone

python3 tcn_main.py --feature_type {}

Run RL alone (one split)

Train:

python3 trpo_train.py --feature_type {} --tcn_run_idx {} --split_idx {} --run_idx {}

Test:

python3 trpo_test.py --feature_type {} --tcn_run_idx {} --split_idx {} --run_idx {}

Output

CSV files: the final results

Folder result: the results in npy format

Folder graph: plots and figures

Folder tcn_featrues: featrues extracted by TCN (states for RL)

Floder tcn_log: Training log for TCN (Visulize with tensorboard)

Folder tcn_model: Trained models for TCN

Folder trpo_model: Trained models for RL

Contact

Any question please contact with Daochang Liu at finspire13@gmail.com

Citation

@InProceedings{10.1007/978-3-030-00937-3_29, author="Liu, Daochang and Jiang, Tingting", title="Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification", booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018", year="2018", publisher="Springer International Publishing", address="Cham", pages="247--255", isbn="978-3-030-00937-3" }