RBAD: A Dataset and Benchmark for Retinal Bifurcation Angle Detection

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

Keywords: Medical Imaging, Retinal Analysis, Image Processing, Medical Dataset

Abstract: Detecting retinal image analysis, particularly the geometrical features of bifurcation 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 bifurcation 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 bifurcation angles. Our methodology for retinal bifurcation 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.

Track: 11. General Track

Registration Id: L8N5WC7SKXQ

Submission Number: 316