The RETA Benchmark for Retinal Vascular Tree Analysis

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis. <br> Our website: https://www.reta-benchmark.org<br>视网膜血管的拓扑与几何分析，可作为检测多种常见疾病的高性价比手段。临床应用中的自动化血管分割与血管树分析模型，需具备强大的泛化能力。本研究构建了一款全新的基准数据集RETA，包含81张标注完成的血管掩码，旨在为视网膜血管分析相关研究提供支持。针对血管标注任务，本研究提出了一种半自动化的由粗到细工作流程。在数据集构建过程中，我们通过自研专用软件开展多阶段标注与标签消歧义工作，力求控制标注者间与标注者自身的标注偏差。除二值血管掩码外，本数据集还涵盖动脉/静脉掩码、血管骨架、分叉点、血管树以及异常区域等多种类型的标注信息。针对标注完成的血管掩码开展的主客观质量验证结果显示，本数据集的标注质量显著优于现有公开数据集。本研究同时公开了自研的标注软件，可用于像素级血管可视化；研究人员可基于RETA开发血管分割算法并评估其分割性能。此外，该数据集还有助于推动跨模态管状结构分割与分析领域的研究。 本研究公开网站：https://www.reta-benchmark.org