Journal
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
Volume 54, Issue 13, Pages 8375-8383Publisher
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/iovs.13-12552
Keywords
segmentation; geographic atrophy; spectral-domain optical coherence tomography images; fundus autofluorescence images
Categories
Funding
- Beckman Macular Degeneration Research Center
- Research to Prevent Blindness Physician Scientist Award
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0844765] Funding Source: National Science Foundation
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PURPOSE. Geographic atrophy (GA) is the atrophic late-stage manifestation of age-related macular degeneration (AMD), which may result in severe vision loss and blindness. The purpose of this study was to develop a reliable, effective approach for GA segmentation in both spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images using a level set-based approach and to compare the segmentation performance in the two modalities. METHODS. To identify GA regions in SD-OCT images, three retinal surfaces were first segmented in volumetric SD-OCT images using a double-surface graph search scheme. A two-dimensional (2-D) partial OCT projection image was created from the segmented choroid layer. A level set approach was applied to segment the GA in the partial OCT projection image. In addition, the algorithm was applied to FAF images for the GA segmentation. Twenty randomly chosen macular SD-OCT (Zeiss Cirrus) volumes and 20 corresponding FAF (Heidelberg Spectralis) images were obtained from 20 subjects with GA. The algorithm-defined GA region was compared with consensus manual delineation performed by certified graders. RESULTS. The mean Dice similarity coefficients (DSC) between the algorithm-and manually defined GA regions were 0.87 +/- 0.09 in partial OCT projection images and 0.89 +/- 0.07 in registered FAF images. The area correlations between them were 0.93 (P < 0.001) in partial OCT projection images and 0.99 (P < 0.001) in FAF images. The mean DSC between the algorithm-defined GA regions in the partial OCT projection and registered FAF images was 0.79 +/- 0.12, and the area correlation was 0.96 (P < 0.001). CONCLUSIONS. A level set approach was developed to segment GA regions in both SD-OCT and FAF images. This approach demonstrated good agreement between the algorithm-and manually defined GA regions within each single modality. The GA segmentation in FAF images performed better than in partial OCT projection images. Across the two modalities, the GA segmentation presented reasonable agreement.
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