OCT-OCTA segmentation: combining structural and blood flow information to segment Bruch's membrane.
Julia SchottenhammlEric M MoultStefan B PlonerSiyu ChenEduardo NovaisLennart HusvogtJay S DukerNadia K WaheedJames G FujimotoAndreas K MaierPublished in: Biomedical optics express (2020)
In this paper we present a fully automated graph-based segmentation algorithm that jointly uses optical coherence tomography (OCT) and OCT angiography (OCTA) data to segment Bruch's membrane (BM). This is especially valuable in cases where the spatial correlation between BM, which is usually not visible on OCT scans, and the retinal pigment epithelium (RPE), which is often used as a surrogate for segmenting BM, is distorted by pathology. We validated the performance of our proposed algorithm against manual segmentation in a total of 18 eyes from healthy controls and patients with diabetic retinopathy (DR), non-exudative age-related macular degeneration (AMD) (early/intermediate AMD, nascent geographic atrophy (nGA) and drusen-associated geographic atrophy (DAGA) and geographic atrophy (GA)), and choroidal neovascularization (CNV) with a mean absolute error of ∼0.91 pixel (∼4.1 μm). This paper suggests that OCT-OCTA segmentation may be a useful framework to complement the growing usage of OCTA in ophthalmic research and clinical communities.
Keyphrases
- optical coherence tomography
- diabetic retinopathy
- age related macular degeneration
- deep learning
- convolutional neural network
- blood flow
- optic nerve
- machine learning
- artificial intelligence
- computed tomography
- neural network
- electronic health record
- big data
- contrast enhanced
- atopic dermatitis
- endothelial cells
- editorial comment
- single cell