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13 March 2019 Deep learning for automated screening and semantic segmentation of age-related and juvenile atrophic macular degeneration
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Abstract
Atrophic age-related macular degeneration (AMD) or geographic atrophy (GA), and atrophic juvenile macular degeneration (JMD) or Stargardt atrophy, have been proven to be the leading cause of blindness respectively in older adults, and in children and young adults. Automated techniques of timely screening and detection of such atrophic diseases would appear to be of critical importance in prevention and early treatment of vision loss. We first developed a deep learning-based automated screening system using the residual networks (ResNet), which can differentiate the eyes with atrophic AMD and JMD from normal eyes on fundus autofluorescene (FAF) images. We further developed another deep learning-based automated system to segment the atrophic AMD and JMD lesions using a fully convolutional neural network - U-Net. Transfer learning based on a pre-trained model was applied for ResNet to facilitate the algorithm training, and excessive data augmentation techniques for both ResNet and U-Net were applied to enhance the algorithm generalization ability. In total, 320 FAF images from normal subjects, 320 with atrophic AMD, and 100 with atrophic JMD were included. The performance of the algorithms were evaluated by comparing with manual gradings by reading center graders. For the screening system, there was no reported algorithm and our algorithm demonstrated a high screening accuracy with 0.98 for atrophic AMD and 0.95 for atrophic JMD. For the segmentation system, our algorithm presented a high overlapping ratio with 0.89 ± 0.06 for atrophic AMD and 0.78 ± 0.17 for atrophic JMD.
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Ziyuan Wang, SriniVas R. Sadda, and Zhihong Hu "Deep learning for automated screening and semantic segmentation of age-related and juvenile atrophic macular degeneration", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501Q (13 March 2019); https://doi.org/10.1117/12.2511538
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