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Multi-scale graph harmonies: Unleashing U-Net's potential for medical image segmentation through contrastive

Jie Wu¹, Jiquan Ma¹, Heran Xi²

¹School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 28, 2024

Summary

This study introduces MSGH, a novel medical image segmentation model using Graph Neural Networks (GNNs) to better capture geometric features. MSGH significantly improves segmentation accuracy across multiple datasets, outperforming existing methods.

Keywords:

Graph contrastive learning Graph neural network Medical image segmentation Multi-scale fusion U-Net

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Image Analysis
Computer Vision
Artificial Intelligence in Healthcare

Background:

Accurate medical image segmentation is crucial for diagnosis, treatment planning, and quantitative analysis.
Existing Convolutional Neural Networks (CNNs) and Transformers struggle with complex geometries and variations in medical images.
Current models often fail to capture intricate geometric features essential for precise segmentation.

Purpose of the Study:

To propose a novel segmentation model, MSGH, leveraging Graph Neural Networks (GNNs) for enhanced geometric representation.
To improve the accuracy and efficiency of automatic medical image segmentation, particularly for complex organ and lesion structures.
To address challenges like category imbalance and intricate detail restoration in medical image segmentation.

Main Methods:

Developed MSGH, a model integrating multi-scale features from Pyramid Feature and Graph Feature branches.
Employed graph contrastive representation learning for self-supervised feature extraction to mitigate category imbalance.
Optimized the decoder with Transformer integration to enhance restoration of fine image details.

Main Results:

MSGH demonstrated significant improvements in dice scores across ACDC, Synapse, and BraTS datasets (2.56-13.41%, 1.04-5.11%, and 1.77-3.35% respectively).
The model consistently outperformed state-of-the-art methods in medical image segmentation tasks.
Experimental validation confirmed the effectiveness and efficiency of the proposed MSGH model.

Conclusions:

The MSGH model effectively utilizes geometric representations via GNNs for superior medical image segmentation.
The integration of multi-scale features, self-supervised learning, and Transformer-enhanced decoding contributes to robust performance.
MSGH represents a significant advancement in AI-assisted healthcare, offering improved accuracy for clinical applications.