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.
@article{arxiv.2407.12271,
title = {RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection},
author = {Hao Wang and Wenhui Zhu and Jiayou Qin and Xin Li and Oana Dumitrascu and Xiwen Chen and Peijie Qiu and Abolfazl Razi},
journal= {arXiv preprint arXiv:2407.12271},
year = {2024}
}