1. A machine learning method for optical coherence tomography scan quality assessment

    A machine learning method for optical coherence tomography scan quality assessment

    Purpose : The reliability of automated analysis of optical coherence tomography (OCT) scans depends on the scan quality. Quality indicators in commercial instruments only provide an overall score and do not provide localized information. Here we demonstrated a quality map using a semi-supervised machine learning technique which can aid with identifying local areas of poor quality. Methods : Our method first computes a set of feature maps using signal strength, signal-to-noise ratio (SNR), and contrast for individual or a group of neighboring A-scans of 580 6x6x2mm OCT volumes with good and poor quality (Fig 1A shows one volume). It then combines the ...

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