Table of Contents

<img src="assets/tecno_logo.png" alt="logo tecno" width=150px style="margin-right:50px" align="right" />

1. About
2. Getting started
   1. Prerequisites
   2. Usage
3. Reference
4. Contact

About

TeCNO performs hierarchical prediction refinement with causal, dilated convolutions for surgical phase recognition and outperforms various state-of-the-art LSTM approaches!

Link to paper: TeCNO Paper

Getting started

Follow these steps to get the code running on your local machine!

Prerequisites

pip install -r requirements.txt

Usage

We are using the publicly available Cholec80 dataset. For training we split the videos into individual frames.

Stage 1 - Train Feature Extractor

Run:

python train.py -c modules/cnn/config/config_feature_extract.yml

This will train your feature extractor and in the Test Step it will extract for each Video the features of all images and save it as .pkl

Stage 2 - Train Temporal Convolutional Network

python train.py -c modules/mstcn/config/config_tcn.yml

Reference

@inproceedings{czempiel2020,
 author    = {Tobias Czempiel and
               Magdalini Paschali and
               Matthias Keicher and
               Walter Simson and
               Hubertus Feussner and
               Seong Tae Kim and
               Nassir Navab},
 title     = {TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional
               Networks},
  booktitle = {Medical Image Computing and Computer Assisted Intervention - {MICCAI}
               2020 - 23nd International Conference, Shenzhen, China, October 4-8,
               2020, Proceedings, Part {III}},
  series    = {Lecture Notes in Computer Science},
  volume    = {12263},
  pages     = {343--352},
  publisher = {Springer},
  year      = {2020},
}

Contact

For any problems and question please open an Issue

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