  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.
