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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

MultiTrans: Multi-branch transformer network for medical image segmentation.

Yanhua Zhang¹, Gabriella Balestra², Ke Zhang³

¹Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, 10129, Italy; School of Astronautics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China.

Computer Methods and Programs in Biomedicine

|June 15, 2024

Summary

This summary is machine-generated.

This study introduces MultiTrans, an efficient Transformer architecture for medical image segmentation. It achieves superior accuracy by effectively processing high-resolution features and aggregating multi-scale global and local information.

Keywords:

Abdominal multi-organ segmentation Cardiac segmentation Deep learning Efficient self-attention Medical image segmentation Multi-branch transformers

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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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Published on: May 20, 2016

Area of Science:

Medical Image Analysis
Deep Learning
Computer Vision

Background:

Convolutional Neural Networks (CNNs) dominate medical image segmentation but struggle with global context.
Transformers offer global context but are computationally inefficient, often requiring downsampling or patch-based processing.
Patch-wise operations limit Transformers in capturing fine-grained, pixel-level details crucial for segmentation.

Purpose of the Study:

To develop a memory- and computation-efficient self-attention module for Transformers in medical image segmentation.
To design a novel Multi-Branch Transformer (MultiTrans) architecture capable of handling multi-scale features.
To improve the extraction of both global context and fine spatial details in medical images.

Main Methods:

Proposed an Efficient Self-Attention (ESA) module to enable reasoning on high-resolution features.
Introduced the Multi-Branch Transformer (MultiTrans) architecture with four parallel Transformer branches at different CNN levels.
Aggregated multi-scale global contexts and multi-scale local features through a hybrid CNN-Transformer approach.

Main Results:

MultiTrans achieved the highest segmentation accuracy across three diverse medical image datasets (Synapse, ACDC, M&Ms).
The proposed Efficient Self-Attention (ESA) significantly reduced training memory (18.77%) and computational complexity (20.68% FLOPs) compared to Standard Self-Attention (SSA).
ESA maintained or slightly improved segmentation accuracy while drastically reducing model parameters (74.07% reduction).

Conclusions:

The MultiTrans network demonstrates robust and generalizable performance on medical image segmentation tasks.
Ablation studies confirm the efficiency and effectiveness of the proposed Efficient Self-Attention (ESA) module.
The developed architecture addresses the limitations of existing methods, offering a more efficient and accurate solution for medical image segmentation.