1. PIPE-Net: A pyramidal-input-parallel-encoding network for the segmentation of corneal layer interfaces in OCT images

    PIPE-Net: A pyramidal-input-parallel-encoding network for the segmentation of corneal layer interfaces in OCT images

    Segmentation of corneal layer interfaces in optical coherence tomography (OCT) images is necessary to generate thickness maps used for cornea diagnosis. In this paper, we propose PIPE-Net, a fully convolutional neural network with a pyramidal input, parallel encoders, and a densely connected decoder to segment four corneal layer interfaces. The pyramidal input is encoded using parallel encoders, which allows the network to process a larger receptive field. The encoders are connected level-wise to the decoder through residual summations. The decoder is densely connected using residual summations between its levels to enhance the gradient flow. We use a linear growth rate ...

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