Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation

by Yueming Jin, Yang Yu, Cheng Chen, Zixu Zhao, Pheng-Ann Heng, Danail Stoyanov.

Introduction

• The Pytorch implementation for our paper 'Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation', accepted at IEEE Transactions on Medical Imaging (TMI), 2022.

Dataset

• We use the dataset EndoVis18 and CaDIS.

Results

• More visual results can be found in this video.

Usage

Check dependencies:

   - pytorch 1.8.0
   - opencv-python
   - tqdm
   - timm
   - pi
   - numpy
   - sklearn

Training process

1. Training Transformer based segmentation model (Intra-video)

• Switch folder $ cd ./seg18/

• Use $ python train_swin.py to start the training; parameter setting and training script refer to exp.sh

2. Training Contrastive model (Inter-video)

• Switch folder $ cd ./pixcontrast_18/

• Use $ sh tools/pixpro_swin_ver.sh to start the training.

3. Fine-tuning the segmentation model (Joint Intra and Inter)

• Switch folder $ cd ./seg18/

• Use $ python train_CL_ft_mswin_sgd_minput.py to start the training; parameter setting and training script refer to exp.sh

Test & Visualization

• Use $ python test.py to test; parameter setting and script can refer to exp.sh

Note:

seg18 and pixcontrast_18 are for EndoVis18; segcata and pixcontrast_cata are for CaDIS. Here, we take EndoVis18 as the example. The usage for CaDIS is similar.

Citation

@ARTICLE{9779714,
  author={Jin, Yueming and Yu, Yang and Chen, Cheng and Zhao, Zixu and Heng, Pheng-Ann and Stoyanov, Danail},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMI.2022.3177077}
}

Questions

For further question about the code or paper, please contact 'ymjin5341@gmail.com'