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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Local-global multi-scale attention network for medical image segmentation.

Minghui Zhu¹, Dapeng Cheng^1,2, Yanyan Mao¹

¹School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China.

Peerj. Computer Science

|September 24, 2025

Summary

This summary is machine-generated.

A new deep learning model, the local-global multi-scale attention network (LGMANet), enhances medical image segmentation by better extracting local and global features. This improves accuracy in identifying critical image components for precise segmentation results.

Keywords:

Efficient multi-scale attention Local and global information extraction Medical image segmentation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Deep learning significantly advances medical image segmentation.
Existing methods struggle with extracting comprehensive local and global image information.
Inaccurate feature selection poses a challenge for current segmentation models.

Purpose of the Study:

To introduce a novel deep learning architecture for improved medical image segmentation.
To address limitations in local and global information extraction and core feature selection.
To enhance the accuracy and efficiency of medical image segmentation.

Main Methods:

Proposed the local-global multi-scale attention network (LGMANet).
Introduced a local-global information processing block (LGIPB) for deep feature mining during downsampling.
Designed an efficient multi-scale reconstruction attention (EMRA) module for core feature extraction and noise suppression.

Main Results:

LGMANet demonstrated superior segmentation performance across multiple datasets.
Achieved high Intersection over Union (IoU) scores: ISIC2018 (85.28%), CVC-ClinicDB (82.67%), BUSI (70.07%), and GLaS (88.90%).
The LGIPB and EMRA modules effectively improved information extraction and feature selection.

Conclusions:

LGMANet offers a promising solution for accurate medical image segmentation.
The novel architecture effectively integrates local and global information processing.
The model shows significant potential for clinical applications requiring precise image segmentation.