VascuConNet:用于血管分割的增强型连接网络。

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Medical & Biological Engineering & Computing Pub Date : 2024-11-01 Epub Date: 2024-06-20 DOI:10.1007/s11517-024-03150-8
Muwei Jian, Ronghua Wu, Wenjin Xu, Huixiang Zhi, Chen Tao, Hongyu Chen, Xiaoguang Li
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引用次数: 0

摘要

医学图像分割通常涉及不同的组织类型和结构,包括血管分割和神经纤维束分割等任务。提高分割结果的连续性是医学图像分割中的一项关键挑战,这是由临床应用的需求驱动的,重点是疾病定位和量化。本研究专门为视网膜血管分割设计了一种新型分割模型,利用血管方向信息、边界约束和连续性约束来提高分割精度。为此,我们将 U-Net 与长短期记忆网络(LSTM)级联。U-Net 的特点是参数数量少、分割效率高,而 LSTM 则具有参数共享能力。此外,我们还在模型底层引入了方向信息增强模块,通过方向卷积算子获得包含方向信息的特征图。此外,我们还设计了一种新的混合损失函数,由连接性损失、边界损失和交叉熵损失组成。实验结果表明,该模型在 CHASE_DB1、DRIVE 和 ARIA 这三个广受认可的视网膜血管分割数据集上取得了出色的分割效果。
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VascuConNet: an enhanced connectivity network for vascular segmentation.

Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model's bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA.

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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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