MATLAB script to enhance, segment and vectorize 3D OCT microangiograms We propose a set of deep learning approaches based on convolutional neural networks (CNNs) to automated enhancement, segmentation and gap-correction of OCTA images, especially of those obtained from the rodent cortex. Additionally, we present a strategy for skeletonizing the segmented OCTA and extracting the underlying vascular graph, which enables the quantitative assessment of various angioarchitectural properties, including individual vessel lengths and tortuosity. Stefan, Sabina Author Lee, Jonghwan Author 2021 deep learning Optical coherence tomography angiography OCTA Segmentation Microvasculature research_datasets datasets 10.26300/15yf-tx16 This research is funded by the NIH National Eye Institute under award R01EY030569 and NIH National Institute on Aging (NIA) under award R01AG067228. This work is licensed under a GNU GPL3 License