1. Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images

    Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images

    Segmenting the retinal layers in optical coherence tomography (OCT) images helps to quantify the layer information in early diagnosis of retinal diseases, which are the main cause of permanent blindness. Thus, the segmentation process plays a critical role in preventing vision impairment. However, because there is a lack of practical automated techniques, expert ophthalmologists still have to manually segment the retinal layers. In this study, we propose an automated segmentation method for OCT images based on a feature-learning regression network without human bias. The proposed deep neural network regression takes the intensity, gradient, and adaptive normalized intensity score (ANIS) of ...

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