1. Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

    Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

    In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on ...

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