Retinal fundus images serve as a non-invasive modality to obtain information pertaining to retinal vessels through fundus photography, thereby offering insights into cardiovascular and cerebrovascular diseases. Retinal arteriolar morphometry has emerged as the most convenient and fundamental clinical methodology in the realm of patient screening and diagnosis. Nevertheless, the analysis of retinal arterioles is challenging attributable to imaging noise, stochastic fuzzy characteristics, and blurred boundaries proximal to blood vessels. In response to these limitations, we introduce an innovative methodology, named PKSEA-Net, which aims to improve segmentation accuracy by enhancing the perception of edge information in retinal fundus images. PKSEA-Net employs the universal architecture PVT-v2 as the encoder, complemented by a novel decoder architecture consisting of an Edge-Aware Block (EAB) and a Pyramid Feature Fusion Module (PFFM). The EAB block incorporates prior knowledge for supervision and multi-query for multi-task learning, with supervision information derived from an enhanced Full Width at Half Maximum (FWHM) algorithm and gradient map. Moreover, PFFM efficiently integrates multi-scale features through a novel attention fusion method. Additionally, we have collected a Retinal Cross-Sectional Vessel (RCSV) dataset derived from approximately 200 patients in Quzhou People’s Hospital to serve as the benchmark dataset. Comparative evaluations with several state-of-the-art (SOTA) networks confirm that PKSEA-Net achieves exceptional experimental performance, thereby establishing its status as a SOTA approach for precise boundary delineation and retinal vessel segmentation.

中文翻译：

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PKSEA-Net：用于视网膜小动脉形态测量的先验知识监督边缘感知多任务网络

视网膜眼底图像作为一种非侵入性方式，通过眼底摄影获取有关视网膜血管的信息，从而深入了解心脑血管疾病。视网膜小动脉形态测量已成为患者筛查和诊断领域最方便和基本的临床方法。然而，由于成像噪声、随机模糊特征和血管附近的模糊边界，视网膜小动脉的分析具有挑战性。针对这些限制，我们引入了一种名为 PKSEA-Net 的创新方法，该方法旨在通过增强视网膜眼底图像中边缘信息的感知来提高分割精度。 PKSEA-Net 采用通用架构 PVT-v2 作为编码器，并辅以由边缘感知块 (EAB) 和金字塔特征融合模块 (PFFM) 组成的新颖解码器架构。 EAB 块结合了用于多任务学习的监督和多查询的先验知识，以及从增强的半高全宽 (FWHM) 算法和梯度图导出的监督信息。此外，PFFM 通过一种新颖的注意力融合方法有效地集成了多尺度特征。此外，我们还收集了来自衢州市人民医院约 200 名患者的视网膜横截面血管 (RCSV) 数据集，作为基准数据集。与几个最先进 (SOTA) 网络的比较评估证实，PKSEA-Net 实现了卓越的实验性能，从而确立了其作为精确边界描绘和视网膜血管分割的 SOTA 方法的地位。