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. Our website: https://www.reta-benchmark.org

视网膜血管的拓扑与几何分析，是检测多种常见疾病的成本效益优良的手段。自动化血管分割与血管树分析模型在临床应用中，需具备优异的泛化能力。本研究构建了一款全新的基准数据集RETA，包含81份带标注的血管掩码，旨在为视网膜血管分析研究提供支持。针对血管标注任务，本研究提出了一种半自动的由粗到细工作流程。在数据集构建过程中，我们依托自研的专用标注软件，通过多阶段标注与标签消歧手段，尽可能控制标注者间与标注者内的标注偏差。除二值血管掩码外，本数据集还涵盖多种其他类型的标注信息，包括动/静脉掩码、血管骨架、分叉点、血管树以及异常结构。对标注血管掩码开展的主客观质量评估结果显示，本数据集的标注质量显著优于现有公开数据集。我们还将自研的标注软件开源，以支持像素级别的血管可视化工作。研究人员可依托RETA开发血管分割算法，并对分割性能进行评估与验证。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发展。本数据集官方网站：https://www.reta-benchmark.org