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SEGMENTATION AND CLASSIFICATION OF GEOGRAPHIC ATROPHY PATTERNS IN PATIENTS WITH AGE RELATED MACULAR DEGENERATION IN WIDEFIELD AUTOFLUORESCENCE IMAGES
SEGMENTATION AND CLASSIFICATION OF GEOGRAPHIC ATROPHY PATTERNS IN PATIENTS WITH AGE RELATED MACULAR DEGENERATION IN WIDEFIELD AUTOFLUORESCENCE IMAGES
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机译：年龄相关性黄斑变性在宽幅自发荧光图像中的地理萎缩模式的分类和分类
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摘要
An automated segmentation and identification system/method for identifying geographic atrophy (GA) phenotypic patterns in fundus autofluorescence images. A hybrid process combines a supervised pixel classifier with an active contour algorithm. A trained, machine learning model (e.g., SVM or U-Net) provides initial GA segmentation/classification, and this is followed by Chan-Vese active contour algorithm. The junctional zones of the GA segmented area are then analyzed for geometric regularity and light intensity regularity. A determination of GA phenotype is made, at least in part, from these parameters.
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