Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos

Introduction

This is a PyTorch implementation of IEEE TMI Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos

In this papper, we design a temporal hierarchical network to generate hierarchical high-level segments to refine low-level frame predictions, based on NETE

Framework visualization

Preparation

Datasets and our trained model

Cholec80, M2CAI16 and our trained model GoogleDrive

Run the code

Installation

  matplotlib==3.5.1
  numpy==1.20.3
  scikit_learn==1.0.2
  seaborn==0.11.2
  thop==0.0.31.post2005241907
  torch==1.9.0
  torchvision==0.10.0
  tqdm==4.61.2

Train the model

 python main.py --action=hierarch_train --hier=True --first=True --trans=True

(The model would be saved in "models/")

Evaluate

Generate predictions

  python main.py --action=hierarch_predict --hier=True --first=True --trans=True

(This would generate predictions in "results/")

Evaluate the predcitions

   matlab-eval/Main.m (cholec80)
   matlab-eval/Main_m2cai.m (m2cai16)

Mean jaccard: 83.53 +- 5.76 Mean accuracy: 91.85 +- 7.55 Mean precision: 91.75 +- 5.46 Mean recall: 91.74 +- 5.77

Mean jaccard: 84.92 +- 7.70 Mean accuracy: 91.99 +- 8.44 Mean precision: 93.74 +- 5.77 Mean recall: 92.88 +- 4.83

Citation

If this code is useful for your research, please citing:

@article{ding2022exploring,
title={Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos},
author={Ding, Xinpeng and Li, Xiaomeng},
journal={IEEE Transactions on Medical Imaging},
year={2022},
publisher={IEEE}
}