Journal
BIOMEDICAL OPTICS EXPRESS
Volume 8, Issue 1, Pages 48-56Publisher
Optica Publishing Group
DOI: 10.1364/BOE.8.000048
Keywords
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Funding
- National Institutes of Health (NIH) [DP3 DK104397, R01 EY024544, R01 EY023285, P30 EY010572]
- Choroideremia Research Foundation
- Research to Prevent Blindness
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The choriocapillaris plays an important role in supporting the metabolic demands of the retina. Studies of the choriocapillaris in disease states with optical coherence tomography angiography (OCTA) have proven insightful. However, image artifacts complicate the identification and quantification of the choriocapillaris in degenerative diseases such as choroideremia. Here, we demonstrate a supervised machine learning approach to detect intact choriocapillaris based on training with results from an expert grader. We trained a random forest classifier to evaluate en face structural OCT and OCTA information along with spatial image features. Evaluation of the trained classifier using previously unseen data showed good agreement with manual grading. (C) 2016 Optical Society of America
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