ArticleIEEE transactions on medical imaging2022
Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- LRNet: Link Residual Neural Network for Blood Vessel Segmentation in OCTA Images.Journal of imaging informatics in medicine · 2025Article
- Dual-stream disentangled model for microvascular extraction in five datasets from multiple OCTA instruments.Frontiers in medicine · 2025Article
- HACA3: A unified approach for multi-site MR image harmonization.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Optical Coherence Tomography Angiography of the Intestine: How to Prevent Motion Artifacts in Open and Laparoscopic Surgery?Life (Basel, Switzerland) · 2023Article
- Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
12 authors.
Funding
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.
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Registered trials
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