Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

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• 作者：Albert K. Feeny^a ; ^b ; Mongkol Tadarati^c ; ^e ; David E. Freund^a ; Neil M. Bressler^c ; Philippe Burlina^a ; ^c ; ^d ;
• 关键词：Automated delineation ; Segmentation ; Geographic atrophy of the retinal pigment epithelium ; Age-related macular degeneration ; AREDS color fundus imagery ; Machine learning
• 刊名：Computers in Biology and Medicine
• 出版年：2015
• 出版时间：1 October 2015
• 年：2015
• 卷：65
• 期：Complete
• 页码：124-136
• 全文大小：7736 K

文摘

Background: Age-related macular degeneration (AMD), left untreated, is the leading cause of vision loss in people older than 55. Severe central vision loss occurs in the advanced stage of the disease, characterized by either the in growth of choroidal neovascularization (CNV), termed the “wet” form, or by geographic atrophy (GA) of the retinal pigment epithelium (RPE) involving the center of the macula, termed the “dry” form. Tracking the change in GA area over time is important since it allows for the characterization of the effectiveness of GA treatments. Tracking GA evolution can be achieved by physicians performing manual delineation of GA area on retinal fundus images. However, manual GA delineation is time-consuming and subject to inter-and intra-observer variability.

Methods: We have developed a fully automated GA segmentation algorithm in color fundus images that uses a supervised machine learning approach employing a random forest classifier. This algorithm is developed and tested using a dataset of images from the NIH-sponsored Age Related Eye Disease Study (AREDS). GA segmentation output was compared against a manual delineation by a retina specialist.

Results: Using 143 color fundus images from 55 different patient eyes, our algorithm achieved PPV of 0.82±0.19, and NPV of 0:95±0.07.

Discussion: This is the first study, to our knowledge, applying machine learning methods to GA segmentation on color fundus images and using AREDS imagery for testing. These preliminary results show promising evidence that machine learning methods may have utility in automated characterization of GA from color fundus images.