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基于高灰度值注意力机制的脑白质高信号分割

张伯泉 麦海鹏 陈嘉敏 逄锦聚

计算机与现代化Issue(12):67-75,9.
计算机与现代化Issue(12):67-75,9.DOI:10.3969/j.issn.1006-2475.2023.12.012

基于高灰度值注意力机制的脑白质高信号分割

White Matter Hyperintensities Segmentation Based on High Gray Value Attention Mechanism

张伯泉 1麦海鹏 1陈嘉敏 1逄锦聚2

作者信息

  • 1. 广东工业大学计算机学院,广东 广州 510006
  • 2. 青岛西海岸新区教育和体育局,山东 青岛 266427
  • 折叠

摘要

Abstract

White matter hyperintensities,commonly seen in the image of cerebral small vessel disease(CSVD),shed light on the clinical diagnoses of patients with cerebral small vessel disease.White matter hyperintensities segmentation,as a basic work in clinical diagnosis,often requires experienced doctors to carry it out manually,which is time-consuming and intricate.White matter hyperintensities,referring to the hyperintense shadows in T2 weighted magnetic resonance images of the brains or fluid-attenuated inversion recovery sequence images,are of higher gray values than other brain tissues.To enhance the attention to ar-eas of white matter hyperintensities,this paper proposes a network model of a high gray value attention mechanism in light of the imaging characteristics of white matter hyperintensities.The model,based on the UNet,introduces a module of high gray value attention so that it can pay more attention to the areas of relatively high gray values in the images.It also introduces a residual mixed attention module to enhance the ability for extracting features of the net model.As a result,it significantly enhances the segmentation effect of white matter hyperintensities,with its DSC and Recall indicators reaching 0.8330 and 0.8870,respec-tively,which is better than existing algorithms.Moreover,ablation experiments verified the effectiveness of the high gray value attention module and the residual hybrid attention module.This paper provides a new method for the FLAIR-based segmentation of white matter hyperintensities lesion,and verifies the feasibility of combining the traditional method for image segmentation with in-depth learning technology.

关键词

脑白质高信号/深度学习/医学图像分割/UNet网络/高灰度值注意力机制

Key words

white matter hyperintensities/deep learning/medical image segmentation/UNet/high gray value attention mechanism

分类

信息技术与安全科学

引用本文复制引用

张伯泉,麦海鹏,陈嘉敏,逄锦聚..基于高灰度值注意力机制的脑白质高信号分割[J].计算机与现代化,2023,(12):67-75,9.

基金项目

国家自然科学基金资助项目(62076074) (62076074)

华为"智能基座"人工智能项目(211210176) (211210176)

计算机与现代化

OACSTPCD

1006-2475

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