Zhu Yuanpei, Liu Yong, Zhou Xuezhi
School of Physics and Electronic Engineering, Xinxiang University, Xinxiang, China.
School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China.
PLoS One. 2025 Sep 8;20(9):e0330641. doi: 10.1371/journal.pone.0330641. eCollection 2025.
Computer-aided diagnostic (CAD) systems for color fundus images play a critical role in the early detection of fundus diseases, including diabetes, hypertension, and cerebrovascular disorders. Although deep learning has substantially advanced automatic segmentation techniques in this field, several challenges persist, such as limited labeled datasets, significant structural variations in blood vessels, and persistent dataset discrepancies, which continue to hinder progress. These challenges lead to inconsistent segmentation performance, particularly for small vessels and branch regions. To address these limitations, we propose an enhanced HarDNet-based model that integrates HarDNet modules, Receptive Field Block (RFB) modules (designed to capture multi-scale contextual information), and Dense Aggregation modules. This innovative architecture enables the network to effectively extract multi-scale features and improve segmentation accuracy, especially for small and complex structures. The proposed model achieves superior performance in retinal vessel segmentation tasks, with accuracies of 0.9685 (±0.0035) on the DRIVE dataset and 0.9744 (±0.0029) on the CHASE_DB1 dataset, surpassing state-of-the-art models such as U-Net, ResU-Net, and R2U-Net. Notably, the model demonstrates exceptional capability in segmenting tiny vessels and branch regions, producing results that closely align with the gold standard. This highlights its significant advantage in handling intricate vascular structures. The robust and accurate performance of the proposed model underscores its effectiveness and reliability in medical image analysis, providing valuable technical support for related research and applications.
用于彩色眼底图像的计算机辅助诊断（CAD）系统在眼底疾病（包括糖尿病、高血压和脑血管疾病）的早期检测中起着关键作用。尽管深度学习在该领域极大地推进了自动分割技术，但仍存在一些挑战，如标记数据集有限、血管结构变化显著以及数据集差异持续存在，这些问题继续阻碍着进展。这些挑战导致分割性能不一致，特别是对于小血管和分支区域。为了解决这些限制，我们提出了一种基于增强型HarDNet的模型，该模型集成了HarDNet模块、感受野块（RFB）模块（旨在捕获多尺度上下文信息）和密集聚合模块。这种创新架构使网络能够有效地提取多尺度特征并提高分割精度，特别是对于小而复杂的结构。所提出的模型在视网膜血管分割任务中取得了优异的性能，在DRIVE数据集上的准确率为0.9685（±0.0035），在CHASE_DB1数据集上的准确率为0.9744（±0.0029），超过了U-Net、ResU-Net和R2U-Net等现有最先进的模型。值得注意的是，该模型在分割微小血管和分支区域方面表现出卓越的能力，产生的结果与金标准非常吻合。这突出了其在处理复杂血管结构方面的显著优势。所提出模型的强大而准确的性能强调了其在医学图像分析中的有效性和可靠性，为相关研究和应用提供了有价值的技术支持。
