利用创新维度压缩技术进行多任务 OCTA 图像分割

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-10-30 DOI:10.1016/j.patcog.2024.111123
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引用次数: 0

摘要

光学相干断层扫描血管造影术(OCTA)在早期检测和持续监测眼部疾病方面发挥着至关重要的作用,这有赖于对视网膜图像进行准确的多组织分割。现有的 OCTA 分割方法通常侧重于单任务设计,不能充分利用这些图像中的体积数据信息。为了弥补这一缺陷,我们的研究引入了 H2C-Net,这是一种新颖的网络架构,可同时精确分割各种视网膜结构,包括毛细血管、动脉、静脉和眼窝无血管区(FAZ)。H2C-Net 的核心包括一个即插即用的高度通道模块(H2C)和一个增强型 U 形网络(GPC-Net)。H2C 模块通过挤压(Squeeze)操作将 OCTA 容积数据的高度信息巧妙地转换为通道信息,实现了从三维到二维的无损降维,并通过单向汇集提供 "软分层 "信息。同时,为了引导网络聚焦于通道进行训练,U-Net 还增强了组归一化、通道关注机制和参数整流线性单元(PReLU),从而降低了对批量大小的依赖,增强了网络提取突出特征的能力。在公开的 OCTA-500 数据集的两个子集上进行的广泛实验表明,H2C-Net 优于现有的最先进方法。它的平均 "联合交叉"(IoU)得分分别为 82.84 % 和 88.48 %,分别提高了 0.81 % 和 1.59 %。同样,平均 Dice 分数也分别提高到 90.40 % 和 93.76 %,比以前的基准高出 0.42 % 和 0.94 %。所提出的 H2C-Net 在 OCTA 图像分割中表现出色,为眼科诊断提供了高效、准确的多任务分割解决方案。代码可在以下网址公开获取:https://github.com/IAAI-SIT/H2C-Net。
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Multi-task OCTA image segmentation with innovative dimension compression
Optical Coherence Tomography Angiography (OCTA) plays a crucial role in the early detection and continuous monitoring of ocular diseases, which relies on accurate multi-tissue segmentation of retinal images. Existing OCTA segmentation methods typically focus on single-task designs that do not fully utilize the information of volume data in these images. To bridge this gap, our study introduces H2C-Net, a novel network architecture engineered for simultaneous and precise segmentation of various retinal structures, including capillaries, arteries, veins, and the fovea avascular zone (FAZ). At its core, H2C-Net consists of a plug-and-play Height-Channel Module (H2C) and an Enhanced U-shaped Network (GPC-Net). The H2C module cleverly converts the height information of the OCTA volume data into channel information through the Squeeze operation, realizes the lossless dimensionality reduction from 3D to 2D, and provides the "Soft layering" information by unidirectional pooling. Meanwhile, in order to guide the network to focus on channels for training, U-Net is enhanced with group normalization, channel attention mechanism, and Parametric Rectified Linear Unit (PReLU), which reduces the dependence on batch size and enhances the network's ability to extract salient features. Extensive experiments on two subsets of the publicly available OCTA-500 dataset have shown that H2C-Net outperforms existing state-of-the-art methods. It achieves average Intersection over Union (IoU) scores of 82.84 % and 88.48 %, marking improvements of 0.81 % and 1.59 %, respectively. Similarly, the average Dice scores are elevated to 90.40 % and 93.76 %, exceeding previous benchmarks by 0.42 % and 0.94 %. The proposed H2C-Net exhibits excellent performance in OCTA image segmentation, providing an efficient and accurate multi-task segmentation solution in ophthalmic diagnostics. The code is publicly available at: https://github.com/IAAI-SIT/H2C-Net.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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