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.

Fig. 1. The architecture of 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

Fig. 2. Visual segmentation results.

Table 1. Segmentation results.

Citation

coming soon