- Title Information
- Title
- MATLAB script to enhance, segment and vectorize 3D OCT microangiograms
- 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.
- Name
- Name Part
- Stefan, Sabina
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Name
- Name Part
- Lee, Jonghwan
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Origin Information
- Date Created
- 2021
- Subject (Local)
- Topic
- deep learning
- Subject (Local)
- Topic
- Optical coherence tomography angiography
- Subject (Local)
- Topic
- OCTA
- Subject (Local)
- Topic
- Segmentation
- Subject (Local)
- Topic
- Microvasculature
- Type of Resource
- software, multimedia
- Genre
- datasets
- Identifier:
DOI
- 10.26300/15yf-tx16
- Note:
funding
- 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 Condition:
use and reproduction
(href="https://www.gnu.org/licenses/gpl.txt")
- This work is licensed under a GNU GPL3 License
- Access Condition:
logo
(href="https://www.gnu.org/graphics/gplv3-88x31.png")