27 February 2018 Automated segmentation of geographic atrophy using deep convolutional neural networks
Author Affiliations +
Abstract
Geographic atrophy (GA) is an end-stage manifestation of the advanced age-related macular degeneration (AMD), the leading cause of blindness and visual impairment in developed nations. Techniques to rapidly and precisely detect and quantify GA would appear to be of critical importance in advancing the understanding of its pathogenesis. In this study, we develop an automated supervised classification system using deep convolutional neural networks (CNNs) for segmenting GA in fundus autofluorescene (FAF) images. More specifically, to enhance the contrast of GA relative to the background, we apply the contrast limited adaptive histogram equalization. Blood vessels may cause GA segmentation errors due to similar intensity level to GA. A tensor-voting technique is performed to identify the blood vessels and a vessel inpainting technique is applied to suppress the GA segmentation errors due to the blood vessels. To handle the large variation of GA lesion sizes, three deep CNNs with three varying sized input image patches are applied. Fifty randomly chosen FAF images are obtained from fifty subjects with GA. The algorithm-defined GA regions are compared with manual delineation by a certified grader. A two-fold cross-validation is applied to evaluate the algorithm performance. The mean segmentation accuracy, true positive rate (i.e. sensitivity), true negative rate (i.e. specificity), positive predictive value, false discovery rate, and overlap ratio, between the algorithm- and manually-defined GA regions are 0.97 ± 0.02, 0.89 ± 0.08, 0.98 ± 0.02, 0.87 ± 0.12, 0.13 ± 0.12, and 0.79 ± 0.12 respectively, demonstrating a high level of agreement.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhihong Hu, Ziyuan Wang, SriniVas R. Sadda, "Automated segmentation of geographic atrophy using deep convolutional neural networks", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057511 (27 February 2018); doi: 10.1117/12.2287001; https://doi.org/10.1117/12.2287001
PROCEEDINGS
9 PAGES + PRESENTATION

SHARE
Back to Top