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

arXiv: https://arxiv.org/abs/2407.12271 · HTML: https://mlsanity.com/paper/2407.12271/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets](https://mlsanity.com/paper/2102.03444/) (2021)
- [OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing](https://mlsanity.com/paper/2302.03003/) (2023)
- [Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images](https://mlsanity.com/paper/2302.02991/) (2023)
- [nnMobileNet: Rethinking CNN for Retinopathy Research](https://mlsanity.com/paper/2306.01289/) (2023)
- [Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation](https://mlsanity.com/paper/2210.05946/) (2022)
- [Fast Key Points Detection and Matching for Tree-Structured Images](https://mlsanity.com/paper/2211.03242/) (2022)
- [Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation](https://mlsanity.com/paper/2404.17031/) (2024)

## Built on by (most cited first, 4 of 4)

- [RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models](https://mlsanity.com/paper/2503.03987/) (2025)
- [Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification](https://mlsanity.com/paper/2412.02825/) (2024)
- [Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion](https://mlsanity.com/paper/2501.00944/) (2025)
- [nnMobileNet++: Towards Efficient Hybrid Networks for Retinal Image Analysis](https://mlsanity.com/paper/2512.01273/) (2025)

## Abstract (arXiv)

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.