1. ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network

    ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network

    Optical coherence tomography (OCT) is extensively used for diagnosis of diabetic macular edema due to its non-invasive imaging based assessment of the retinal layers. In this paper, we propose a new fully convolutional deep learning architecture, termed ReLayNet, for segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a heirarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. Additionally, skip connections relaying encoder outputs to matched decoder inputs are introduced to recover spatial information lost during downsampling. ReLayNet is trained ...

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