1. Automated segmentation of pathological cavities in optical coherence tomography scans

    Automated segmentation of pathological cavities in optical coherence tomography scans

    PURPOSE: To develop and evaluate a method for automated segmentation and quantitative analysis of pathological cavities in the retina visualized by Spectral-Domain (SD)-OCT scans. METHODS: The algorithm is based on the segmentation of the grey-level intensities within a B-Scan by a k-means cluster analysis and subsequent classification by a k-nearest neighbor algorithm. Accuracy was evaluated against three clinical experts using 130 bullous cavities identified on 8 SD-OCT B-scans of 3 patients with wet age-related macular degeneration (AMD) and 5 patients with X-linked retinoschisis, as well as on one volume scan of a patient with X-linked retinoschisis. The algorithm calculated ...

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