# Towards a glaucoma risk index based on simulated hemodynamics from fundus images

arXiv: https://arxiv.org/abs/1805.10273 · HTML: https://mlsanity.com/paper/1805.10273/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 30 of 31)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge](https://mlsanity.com/paper/2302.01738/) (2023)
- [Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification](https://mlsanity.com/paper/2103.09097/) (2021)
- [The RETA Benchmark for Retinal Vascular Tree Analysis](https://mlsanity.com/paper/2111.11658/) (2021)
- [RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging](https://mlsanity.com/paper/2203.14928/) (2022)
- [Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis](https://mlsanity.com/paper/2409.04768/) (2024)
- [Learning to Address Intra-segment Misclassification in Retinal Imaging](https://mlsanity.com/paper/2104.12138/) (2021)
- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images](https://mlsanity.com/paper/2209.09582/) (2022)
- [RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification](https://mlsanity.com/paper/2402.03166/) (2024)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Models](https://mlsanity.com/paper/2508.03734/) (2025)
- [Self-Supervised Learning from Unlabeled Fundus Photographs Improves Segmentation of the Retina](https://mlsanity.com/paper/2108.02798/) (2021)
- [Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2507.17281/) (2025)
- [Improve Retinal Artery/Vein Classification via Channel Couplin](https://mlsanity.com/paper/2508.03738/) (2025)
- [Rotterdam artery-vein segmentation (RAV) dataset](https://mlsanity.com/paper/2512.17322/) (2025)
- [VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation](https://mlsanity.com/paper/2203.06425/) (2022)
- [The Topology-Overlap Trade-Off in Retinal Arteriole-Venule Segmentation](https://mlsanity.com/paper/2303.18022/) (2023)
- [JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection](https://mlsanity.com/paper/2305.11504/) (2023)
- [Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction](https://mlsanity.com/paper/2411.15251/) (2024)
- [Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling](https://mlsanity.com/paper/2503.01190/) (2025)
- [GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains](https://mlsanity.com/paper/2503.05991/) (2025)
- [Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis](https://mlsanity.com/paper/2504.04549/) (2025)
- [Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning](https://mlsanity.com/paper/2508.18186/) (2025)
- [HiPerformer: A High-Performance Global-Local Segmentation Model with Modular Hierarchical Fusion Strategy](https://mlsanity.com/paper/2509.20280/) (2025)
- [TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomy](https://mlsanity.com/paper/2603.09217/) (2026)
- [FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation](https://mlsanity.com/paper/2603.11659/) (2026)
- [Personalized Federated Learning with Residual Fisher Information for Medical Image Segmentation](https://mlsanity.com/paper/2603.14848/) (2026)
- [MARVEL: Universal Murray's Law-informed Vessel Tree Segmentation and Topology Estimation](https://mlsanity.com/paper/2605.25363/) (2026)
- [CSGen: A Multi-Domain Curvilinear Structure Generation Model via Hierarchical Multimodal Diffusion](https://mlsanity.com/paper/2608.04655/) (2026)

## Abstract (arXiv)

Glaucoma is the leading cause of irreversible but preventable blindness in the world. Its major treatable risk factor is the intra-ocular pressure, although other biomarkers are being explored to improve the understanding of the pathophysiology of the disease. It has been recently observed that glaucoma induces changes in the ocular hemodynamics. However, its effects on the functional behavior of the retinal arterioles have not been studied yet. In this paper we propose a first approach for characterizing those changes using computational hemodynamics. The retinal blood flow is simulated using a 0D model for a steady, incompressible non Newtonian fluid in rigid domains. The simulation is performed on patient-specific arterial trees extracted from fundus images. We also propose a novel feature representation technique to comprise the outcomes of the simulation stage into a fixed length feature vector that can be used for classification studies. Our experiments on a new database of fundus images show that our approach is able to capture representative changes in the hemodynamics of glaucomatous patients. Code and data are publicly available in https://ignaciorlando.github.io.