  Purpose: To demonstrate that retinal microvasculature per se is a reliable
biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular
diseases. Methods: Deep Learning Convolutional Neural Networks (CNN) applied to
color fundus images for semantic segmentation of the blood vessels and severity
classification on both vascular and full images. Vessel reconstruction through
harmonic descriptors is also used as a smoothing and de-noising tool. The
mathematical background of the theory is also outlined. Results: For diabetic
patients, at least 93.8% of DR No-Refer vs. Refer classification can be related
to vasculature defects. As for the Non-Sight Threatening vs. Sight Threatening
case, the ratio is as high as 96.7%. Conclusion: In the case of DR, most of the
disease biomarkers are related topologically to the vasculature. Translational
Relevance: Experiments conducted on eye blood vasculature reconstruction as a
biomarker shows a strong correlation between vasculature shape and later stages
of DR.
