Paper ID: 2407.12271

RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection

Hao Wang, Wenhui Zhu, Jiayou Qin, Xin Li, Oana Dumitrascu, Xiwen Chen, Peijie Qiu, Abolfazl Razi

Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for efficient annotation. To mitigate these issues, this paper proposes a novel method for detecting retinal branching angles using a self-configured image processing technique. Additionally, we offer an open-source annotation tool and a benchmark dataset comprising 40 images annotated with retinal branching angles. Our methodology for retinal branching angle detection and calculation is detailed, followed by a benchmark analysis comparing our method with previous approaches. The results indicate that our method is robust under various conditions with high accuracy and efficiency, which offers a valuable instrument for ophthalmic research and clinical applications.

Submitted: Jul 17, 2024