# Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images

arXiv: https://arxiv.org/abs/2202.09708 · HTML: https://mlsanity.com/paper/2202.09708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 20 Feb 2022.

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

- [Persistence Images: A Stable Vector Representation of Persistent Homology](https://mlsanity.com/paper/1507.06217/) (2015)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [A roadmap for the computation of persistent homology](https://mlsanity.com/paper/1506.08903/) (2015)
- [TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images](https://mlsanity.com/paper/2101.08398/) (2021)
- [NEFI: Network Extraction From Images](https://mlsanity.com/paper/1502.05241/) (2015)
- [Analysis of Spatial and Spatiotemporal Anomalies Using Persistent Homology: Case Studies with COVID-19 Data](https://mlsanity.com/paper/2107.09188/) (2021)
- [Topological Approximate Bayesian Computation for Parameter Inference of an Angiogenesis Model](https://mlsanity.com/paper/2108.11640/) (2021)

## Built on by (most cited first, 1 of 1)

- [SSRCA: a novel machine learning pipeline to perform sensitivity analysis for agent-based models](https://mlsanity.com/paper/2506.00168/) (2025)

## Abstract (arXiv)

Disease complications can alter vascular network morphology and disrupt tissue functioning. Diabetic retinopathy, for example, is a complication of types 1 and 2 diabetes mellitus that can cause blindness. Microvascular diseases are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. We apply our methods to three publicly-available datasets and find that, among the 13 total descriptor vectors we consider, either a statistical Box-counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels on these datasets. We then created a fourth dataset by merging several datasets: the Box-counting vector outperforms all descriptors on this dataset, including the topological Flooding vector which is sensitive to differences in the annotation styles within the combined dataset. Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease as well as some of their current limitations. In the longer term, these methods could be incorporated into automated disease assessment tools.