# The RETA Benchmark for Retinal Vascular Tree Analysis

arXiv: https://arxiv.org/abs/2111.11658 · HTML: https://mlsanity.com/paper/2111.11658/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Towards a glaucoma risk index based on simulated hemodynamics from fundus images](https://mlsanity.com/paper/1805.10273/) (2018)
- [Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks](https://mlsanity.com/paper/1706.09318/) (2017)
- [Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images](https://mlsanity.com/paper/2012.09250/) (2020)
- [Topological Similarity Index and Loss Function for Blood Vessel Segmentation](https://mlsanity.com/paper/2107.14531/) (2021)
- [A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers](https://mlsanity.com/paper/2111.03853/) (2021)
- [On the Evaluation and Real-World Usage Scenarios of Deep Vessel Segmentation for Retinography](https://mlsanity.com/paper/1909.03856/) (2019)

## Built on by (most cited first, 13 of 13)

- [Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2303.05745/) (2023)
- [Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models](https://mlsanity.com/paper/2305.06813/) (2023)
- [3D Vessel Graph Generation Using Denoising Diffusion](https://mlsanity.com/paper/2407.05842/) (2024)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel](https://mlsanity.com/paper/2507.15223/) (2025)
- [FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures](https://mlsanity.com/paper/2404.00130/) (2024)
- [PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation](https://mlsanity.com/paper/2605.17773/) (2026)
- [TFFM: Topology-Aware Feature Fusion Module via Latent Graph Reasoning for Retinal Vessel Segmentation](https://mlsanity.com/paper/2601.19136/) (2026)
- [TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomy](https://mlsanity.com/paper/2603.09217/) (2026)
- [Effect of Input Resolution on Retinal Vessel Segmentation Performance: An Empirical Study Across Five Datasets](https://mlsanity.com/paper/2604.02977/) (2026)
- [Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images](https://mlsanity.com/paper/2605.00538/) (2026)
- [CSGen: A Multi-Domain Curvilinear Structure Generation Model via Hierarchical Multimodal Diffusion](https://mlsanity.com/paper/2608.04655/) (2026)
- [CGDD-Net: Context-Guided Dynamic Detail Modeling for Retinal Vessel Segmentation](https://mlsanity.com/paper/2610.05078/) (2026)

## Abstract (arXiv)

Topological and geometrical analysis of retinal blood vessel is a cost-effective way for early detection of many common diseases. Meanwhile, automated vessel segmentation and vascular tree analysis are still lacking in terms of generalization capability. In this work, we construct a novel benchmark RETA with 81 labeled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow is proposed to annotating vessel pixels. During dataset construction, we strived to control inter-annotator variability and intra-annotator variability by performing multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained vessel annotations containing artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities during vessel labelling. Both subjective and objective quality validation of labeled vessel masks have demonstrated significant improved quality over other publicly datasets. The annotation software is also made publicly available for vessel annotation visualization. Users could develop vessel segmentation algorithms or evaluate vessel segmentation performance with our dataset. Moreover, our dataset might be a good research source for cross-modality tubular structure segmentation.