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Macular layer thicknesses measured on SD-OCT images in healthy eyes showed significant variations by sex and age. These findings should inform macular layer thickness analyses in SD-OCT studies of retinal diseases and glaucoma.

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A novel automated boundary segmentation algorithm is proposed for fast and reliable quantification of nine intra-retinal boundaries in optical coherence tomography (OCT) images. The algorithm employs a two-step segmentation schema based on gradient information in dual scales, utilizing local and complementary global gradient information simultaneously. A shortest path search is applied to optimize the edge selection. The segmentation algorithm was validated with independent manual segmentation and a reproducibility study. It demonstrates high accuracy and reproducibility in segmenting normal 3D OCT volumes. The execution time is about 16 seconds per volume (480×512×128 voxels). The algorithm shows potential for quantifying images from diseased retinas as well.