基于融合注意力的左心房阑尾分割。

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-10 DOI:10.1007/s11517-024-03104-0
Guodong Zhang, Kaichao Liang, Yanlin Li, Tingyu Liang, Zhaoxuan Gong, Ronghui Ju, Dazhe Zhao, Zhuoning Zhang
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引用次数: 0

摘要

在临床实践中,左心房阑尾(LAA)的形态对选择 LAA 关闭装置进行 LAA 关闭手术起着重要作用。形态的确定受到分割结果的影响。LAA 只占整个三维医学图像的一小部分,分割结果更容易偏向背景区域,因此 LAA 的分割具有挑战性。在本文中,我们提出了一种模仿人类视觉行为的轻量级注意力机制--融合注意力。我们采用一种先概览观察后细节观察的方法来处理 LAA 的三维图像。在概览观察阶段,图像特征沿着长、宽、高三个维度汇集。然后将从三个维度获得的特征分别输入空间注意模块和通道注意模块,以了解感兴趣的区域。在详细观察阶段,前一阶段的注意力结果将通过元素乘法进行融合,并与原始特征图相结合,以加强特征学习。在辽宁省人民医院提供的左心房阑尾数据集上对融合注意力机制进行了评估,结果显示平均骰子系数为 0.8855。结果表明,与现有的轻量级注意力机制相比,融合注意力机制在三维图像上取得了更好的分割效果。
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Segmentation of the left atrial appendage based on fusion attention.

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People's Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms.

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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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