RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection
<|reference_start|>RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection: 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.<|reference_end|>
@article{wang2024rbad:,
    title={RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle
  Detection},
    author={Hao Wang, Wenhui Zhu, Jiayou Qin, Xin Li, Oana Dumitrascu, Xiwen Chen,
  Peijie Qiu, Abolfazl Razi},
    journal={arXiv preprint arXiv:2407.12271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12271},
    primaryClass={cs.CV eess.IV}
}