by Zixu Zhao,Yueming Jin, Xiaojie Gao, Qi Dou, Pheng-Ann Heng.
We use the dataset from MICCAI 2017 Endoscopic vision challenge.
Please follow the data preprocessing steps in this repository.
The structure of data folder is arranged as follows:
(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:
2. Flow prediction & Flow compensation (./motion_learning/):
$ python train_mp.py
.$ python train_mc.py
.train.sh
are in default settings.$ bash propagate.sh
.$ bash train.sh
Note: You may try other models from /Models/plane_model.py
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}
}
For further question about the code or paper, please contact 'zxzhao@cse.cuhk.edu.hk'