首页|语义流引导采样结合注意力机制的脑肿瘤图像分割

语义流引导采样结合注意力机制的脑肿瘤图像分割

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U型网络结构的脑肿瘤自动分割方法由于多次卷积和采样操作会造成信息损失,导致分割效果不佳.为解决这一问题,提出了能够利用语义信息流引导上采样特征恢复的特征对齐单元,并在此基础上设计轻量级的双重注意力特征对齐网络(DAFANet).首先,将特征对齐单元分别引入3D UNet、DMFNet和HDCNet三个经典网络,以验证其有效性和泛化性.其次,在DMFNet基础上构造轻量级的双重注意力特征对齐网络DAFANet,利用特征对齐单元强化上采样过程中的特征恢复,3D期望最大化注意力机制同时作用于特征对齐路径和级联路径,用于重点获取上下文的全程依赖关系.同时使用广义Dice损失函数提升数据不平衡时的分割精度并加快模型收敛.最后,在BraTS2018和BraTS2019公开数据集进行验证,文中所提算法在ET,WT和TC区域的分割精度分别达到80.44%,90.07%,84.57%和78.11%,90.10%,82.21%.相较于当前流行的分割网络,具有对增强肿瘤区域更好的分割效果,更擅长处理细节和边缘信息.
Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism
The automatic segmentation method for brain tumors based on a U-shaped network structure of-ten suffers from information loss due to multiple convolution and sampling operations,resulting in subopti-mal segmentation results.To address this issue,this study proposed a feature alignment unit that utilizes semantic information flow to guide the up-sampling feature recovery and design designed a lightweight Du-al Attention Feature Alignment Network(DAFANet)based on this unit.Firstly,to validate its effective-ness and generalization,the feature alignment unit was introduced separately into three classic networks,namely 3D UNet,DMFNet,and HDCNet.Secondly,a lightweight dual-attention feature alignment net-work named DAFANet was proposed based on DMFNet.The feature alignment unit enhanced feature restoration in the up-sampling process,and a 3D Expectation-Maximization attention mechanism was ap-plied to both the feature alignment path and cascade path to capture the full contextual dependency.The generalized Dice loss function was also used to improve segmentation accuracy in the case of data imbal-ance and accelerate model convergence.Finally,the proposed algorithm is validated on the BraTS2018 and BraTS2019 public datasets,achieving segmentation accuracies of 80.44%,90.07%,84.57%and 78.11%,90.10%,82.21%in the ET,WT,and TC regions,respectively.Compared to current popu-lar segmentation networks,the proposed algorithm demonstrates better segmentation performance in en-hancing tumor regions and is more adept at handling details and edge information.

brain tumorsimage segmentationfeature alignmentattention mechanismlightweight

宋建丽、吕晓琪、谷宇

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内蒙古科技大学 信息工程学院,内蒙古 包头市 014010

内蒙古工业大学 信息工程学院,内蒙古 呼和浩特 010051

脑肿瘤 图像分割 特征对齐 注意力机制 轻量化

国家自然科学基金国家自然科学基金国家自然科学基金中央引导地方科技发展资金项目内蒙古自治区高等学校青年科技英才支持项目内蒙古自治区自然科学基金内蒙古自治区自然科学基金内蒙古科技大学基本科研业务费专项优秀青年基金内蒙古自治区高等学科科学技术研究项目教育部"春晖计划"合作科研项目

6200125561841204617712662021ZY0004NJYT230572019MS060032015MS0604042NJZY145教外司留[2019]1383号

2024

10.37188/OPE.20243204.0565

光学精密工程
中国科学院长春光学精密机械与物理研究所 中国仪器仪表学会

光学精密工程

CSTPCD北大核心
影响因子：2.059
ISSN：1004-924X
年,卷(期)：2024.32(4)
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