1. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Image

    Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Image

    Purpose : To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. Methods : Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm 3 ) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate ...

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