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MATLAB script to enhance, segment and vectorize 3D OCT microangiograms

Description

Abstract:
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.
Notes:
This research is funded by the National Institutes of Health (NIH) National Eye Institute under award R01EY030569 and National Institute on Aging (NIA) under award R01AG067228.

Access Conditions

Use and Reproduction
This work is licensed under a GNU GPL3 License

Citation

Stefan, Sabina, and Lee, Jonghwan, "MATLAB script to enhance, segment and vectorize 3D OCT microangiograms" (2021). (NIH R01AG067228) Long-Term Tracking of Cerebral Microvascular Structural and Functional Alterations between Normal and Alzheimer's Aging, Brown University Open Data Collection. Brown Digital Repository. Brown University Library. https://doi.org/10.26300/15yf-tx16

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