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

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 four datasets that we assembled from four existing data repositories; three datasets contain data from one of the repositories, whereas the fourth "All" dataset combines data from four repositories. We apply our methods to the three single-repository 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. We then apply our methods to the "All" dataset; on this combined dataset, the Box-counting vector outperforms all descriptors, including the topological Flooding vector which is sensitive to differences in the annotation styles between the different datasets. 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.