Abstract
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
Original language | English |
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Pages (from-to) | 2732-2744 |
Number of pages | 13 |
Journal | Biomedical Optics Express |
Volume | 8 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 May 2017 |
Externally published | Yes |
Keywords
- Image analysis
- Image processing
- Optical coherence tomography