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Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data.
- Source :
-
IEEE Transactions on Medical Imaging . Dec2022, Vol. 41 Issue 12, p3686-3698. 13p. - Publication Year :
- 2022
-
Abstract
- Optical coherence tomography angiography (OCTA) is an imaging modality that can be used for analyzing retinal vasculature. Quantitative assessment of en face OCTA images requires accurate segmentation of the capillaries. Using deep learning approaches for this task faces two major challenges. First, acquiring sufficient manual delineations for training can take hundreds of hours. Second, OCTA images suffer from numerous contrast-related artifacts that are currently inherent to the modality and vary dramatically across scanners. We propose to solve both problems by learning a disentanglement of an anatomy component and a local contrast component from paired OCTA scans. With the contrast removed from the anatomy component, a deep learning model that takes the anatomy component as input can learn to segment vessels with a limited portion of the training images being manually labeled. Our method demonstrates state-of-the-art performance for OCTA vessel segmentation. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DEEP learning
*OPTICAL coherence tomography
*TRAINING manuals
*FACE
Subjects
Details
- Language :
- English
- ISSN :
- 02780062
- Volume :
- 41
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Medical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 160651477
- Full Text :
- https://doi.org/10.1109/TMI.2022.3193029