Automated Quantification of Macular Vasculature Changes from OCTA Images of Hematologic Patients

Abstract

Abnormal blood compositions can lead to abnormal blood flow which can influence the macular vasculature. Optical coherence tomography angiography (OCTA) makes it possible to study the macular vasculature and potential vascular abnormalities induced by hematological disorders. Here, we investigate vascular changes in control subjects and in hematologic patients before and after treatment. Since these changes are small, they are difficult to notice in the OCTA images. To quantify vascular changes, we propose a method for combined capillary registration, dictionary-based segmentation and local density estimation. Using this method, we investigate three patients and five controls, and our results show that we can detect small changes in the vasculature in patients with large changes in blood composition.

Dictionary-based Method for Vascular Segmentation for OCTA Images

“…Instead, we propose to use the dictionary-based segmentation method from [2,3,4], where the segmentation model is learned from annotated training data. We have used this method for segmenting retinal microvasculature from OCTA images in [9,10,11]. In the segmentation part, we assign the dictionary to an input image (S1) and then compute pixel-wise probabilities of the labels using the dictionary labels (S2).…”