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Learning Motion Flows for Semi-supervised Instrument Segmentation from Surgical Robotic Videos

by Zixu Zhao,Yueming Jin, Xiaojie Gao, Qi Dou, Pheng-Ann Heng.

Introduction

  • The Code contains two parts: motion learning (flow prediction and flow compensation) and semi-supervised segmentation.

Data Preparation

(root folder)
├── data
|  ├── cropped_train
|  |  ├── instrument_dataset_1
|  |  |  ├── images
|  |  |  ├── binary_masks
|  |  |  ├── parts_masks
|  |  |  ├── instrument_masks
|  |  ├── instrument_dataset_2
|  |  |  ├── images
|  |  |  ├── binary_masks
|  |  |  ├── parts_masks
|  |  |  ├── instrument_masks
|  |  ├── ......

Setup & Usage for the Code

  1. Check dependencies:
- Python 3.6
- pytorch 0.4.1+
- pytorch-ignite 0.2.0+
- tensorboardX
- albumentations
- opencv-python
- cupy (please check your CUDA version before install)
- tqdm
  1. Flow prediction & Flow compensation (./motion_learning/):
  • To train the flow prediction model, run $ python train_mp.py.
  • To train the flow compensation model, run $ python train_mc.py.
  • Arguments for model training in train.sh are in default settings.
  • To propagate frame-label pairs, run $ bash propagate.sh.
  1. Semi-supervised segmentation (./segmentation/):
$ bash train.sh

Note: You may try other models from /Models/plane_model.py

Citation

If this repository is useful for your research, please cite:

@inproceedings{zhao2020learning,
  title={Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video},
  author={Zhao, Zixu and Jin, Yueming and Gao, Xiaojie and Dou, Qi and Heng, Pheng-Ann},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={679--689},
  year={2020},
  organization={Springer}
}

Questions

For further question about the code or paper, please contact '[email protected]'

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