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 type 1 and 2 diabetus 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 either statistical or topological summaries of segmented retinal vascular images. We apply our methods to four publicly-available datasets and find that the fractal dimension performs best for high resolution images. By contrast, we find that topological descriptor vectors quantifying the number of loops in the data achieve the highest accuracy for low resolution images. Further analysis, using the topological approach, reveals that microvascular disease may alter morphology by reducing the number of loops in the retinal vasculature. Our work provides preliminary guidelines on which methods are most appropriate for assessing disease in high and low resolution images. In the longer term, these methods could be incorporated into automated disease assessment tools.