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Optical coherence tomography (OCT) is a commonly-used method of extractinghigh resolution retinal information. Moreover there is an increasing demand forthe automated retinal layer segmentation which facilitates the retinal diseasediagnosis. In this paper, we propose a novel multiprediction guided attentionnetwork (MPG-Net) for automated retinal layer segmentation in OCT images. Theproposed method consists of two major steps to strengthen the discriminativepower of a U-shape Fully convolutional network (FCN) for reliable automatedsegmentation. Firstly, the feature refinement module which adaptivelyre-weights the feature channels is exploited in the encoder to capture moreinformative features and discard information in irrelevant regions.Furthermore, we propose a multi-prediction guided attention mechanism whichprovides pixel-wise semantic prediction guidance to better recover thesegmentation mask at each scale. This mechanism which transforms the deepsupervision to supervised attention is able to guide feature aggregation withmore semantic information between intermediate layers. Experiments on thepublicly available Duke OCT dataset confirm the effectiveness of the proposedmethod as well as an improved performance over other state-of-the-artapproaches.

MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images