SEGMENTATION AND CLASSIFICATION OF GEOGRAPHIC ATROPHY PATTERNS IN PATIENTS WITH AGE RELATED MACULAR DEGENERATION IN WIDEFIELD AUTOFLUORESCENCE IMAGES

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.