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Disease complications can alter vascular network morphology and disrupttissue functioning. Diabetic retinopathy, for example, is a complication oftypes 1 and 2 diabetes mellitus that can cause blindness. Microvasculardiseases are assessed by visual inspection of retinal images, but this can bechallenging when diseases exhibit silent symptoms or patients cannot attendin-person meetings. We examine the performance of machine learning algorithmsin detecting microvascular disease when trained on statistical and topologicalsummaries of segmented retinal vascular images. We apply our methods to threepublicly-available datasets and find that, among the 13 total descriptorvectors we consider, either a statistical Box-counting descriptor vector or atopological Flooding descriptor vector achieves the highest accuracy levels onthese datasets. We then created a fourth dataset by merging several datasets:the Box-counting vector outperforms all descriptors on this dataset, includingthe topological Flooding vector which is sensitive to differences in theannotation styles within the combined dataset. Our work represents a first stepto establishing which computational methods are most suitable for identifyingmicrovascular disease as well as some of their current limitations. In thelonger term, these methods could be incorporated into automated diseaseassessment tools.

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