Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images
This work addresses automated disease detection for patients with conditions like diabetes, but it is incremental as it compares existing computational methods without introducing new paradigms.
The study tackled the problem of detecting microvascular diseases like diabetic retinopathy by evaluating machine learning algorithms trained on statistical and topological summaries of segmented retinal vascular images, finding that a Box-counting descriptor vector achieved the highest accuracy on a merged dataset.
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.