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A multi-scale information fusion medical image segmentation network based on convolutional kernel coupled updata

Zhihao Lu¹, Jinglan Zhang², Biao Cai¹

¹College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China.

Computers in Biology and Medicine

|January 29, 2025

Summary

This study introduces TransDLNet, a novel network for medical image segmentation that improves multi-scale feature fusion using integrated convolutional neural networks and Transformers. The method demonstrates strong segmentation performance and generalization across diverse medical imaging datasets.

Keywords:

Deep learning Dynamically updated convolution kernels Medical image segmentation Multi-scale features Transformer

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate medical image segmentation is crucial for disease diagnosis and treatment planning.
Existing methods often struggle with efficiently utilizing multi-scale information in complex medical images.

Purpose of the Study:

To present TransDLNet, a novel network architecture designed to enhance multi-scale information utilization for medical image segmentation.
To improve the efficiency and accuracy of segmentation for complex medical imaging data.

Main Methods:

Integration of convolutional neural networks (CNNs) and Transformers for cross-level multi-scale information fusion.
Introduction of the attention-dilated depthwise convolution (ADDC) module for enhanced local detail capture.
Development of the cross-level grouped attention merge (CGAM) module for improved feature interaction across scales.

Main Results:

TransDLNet demonstrated effective segmentation performance on four diverse medical imaging datasets.
The proposed method showed good generalization ability across different imaging modalities.
Experimental analysis confirmed the efficacy of the ADDC and CGAM modules in feature representation and fusion.

Conclusions:

TransDLNet offers an efficient approach to medical image segmentation by enhancing multi-scale feature utilization.
The novel architecture and modules contribute to robust feature representation and comprehensive understanding of medical images.
The findings suggest TransDLNet's potential for improving diagnostic and treatment processes in clinical settings.