Min_Max_Similarity

A contrastive learning based semi-supervised segmentation network for medical image segmentation This repository contains the implementation of a novel contrastive learning based semi-segmentation networks to segment the surgical tools.

Result

Fig. 1. The architecture of Min-Max Similarity.

? NEWS ? The full paper is available: Min-Max Similarity

Environment

  • python==3.6
  • packages:
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

conda install opencv-python pillow numpy matplotlib
  • Clone this repository
git clone https://github.com/AngeLouCN/Min_Max_Similarity

Data Preparation

We use three dataset to test its performance:

  • Kvasir-instrument
  • EndoVis’17
  • Cochlear Implant

File structure

|-- data
|   |-- kvasir
|   |   |-- train
|   |   |   |--image
|   |   |   |--mask
|   |   |-- test
|   |   |   |--image
|   |   |   |--mask
|   |-- EndoVis17
|   |   |-- train
|   |   |   |--image
|   |   |   |--mask
|   |   |-- test
|   |   |   |--image
|   |   |   |--mask
|   |-- cochlear
|   |   |-- train
|   |   |   |--image
|   |   |   |--mask
|   |   |-- test
|   |   |   |--image
|   |   |   |--mask

You can also test on some other public medical image segmentation dataset with above file architecture

Usage

  • Training: You can change the hyper-parameters like labeled ratio, leanring rate, and e.g. in train_mms.py, and directly run the code.

  • Testing: You can change the dataset name in test.py and run the code.

Segmentation Performance

Result

Fig. 2. Visual segmentation results.

Result

Table 1. Segmentation results.

Citation

@article{lou2022min,
  title={Min-Max Similarity: A Contrastive Learning Based Semi-Supervised Learning Network for Surgical Tools Segmentation},
  author={Lou, Ange and Yao, Xing and Liu, Ziteng and Noble, Jack},
  journal={arXiv preprint arXiv:2203.15177},
  year={2022}
}

GitHub

View Github