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.

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

🔥 NEWS 🔥 The paper has been accepted by IEEE Transactions on Medical Imaging. The early access is available at Here.

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 five dataset to test its performance:

• Kvasir-instrument
• EndoVis'17
• Cochlear Implant
• RoboTool
• ART-NET

File structure

|-- data
|   |-- kvasir
|   |   |-- train
|   |   |   |--image
|   |   |   |--mask
|   |   |-- test
|   |   |   |--image
|   |   |   |--mask
|   |-- EndoVis17
|   |   |-- 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 comparison of our method with state-of-the-art models. Segmentation results are shown for 50% of labeled training data for Kvasir-instrument, EndVis’17, ART-NET and RoboTool, and 2.4% labeled training data for cochlear implant. From left to right are EndoVis’17, Kvasir-instrument, ART-NET, RoboTool, Cochlear implant and region of interest (ROI) of Cochlear implant.

Citation

@article{lou2023min,
  title={Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation},
  author={Lou, Ange and Tawfik, Kareem and Yao, Xing and Liu, Ziteng and Noble, Jack},
  journal={IEEE Transactions on Medical Imaging},
  year={2023},
  publisher={IEEE}
}

Acknowledgement

Our code is based on the Duo-SegNet, we thank their excellent work and repository.