  Optical coherence tomography (OCT) is a commonly-used method of extracting
high resolution retinal information. Moreover there is an increasing demand for
the automated retinal layer segmentation which facilitates the retinal disease
diagnosis. In this paper, we propose a novel multiprediction guided attention
network (MPG-Net) for automated retinal layer segmentation in OCT images. The
proposed method consists of two major steps to strengthen the discriminative
power of a U-shape Fully convolutional network (FCN) for reliable automated
segmentation. Firstly, the feature refinement module which adaptively
re-weights the feature channels is exploited in the encoder to capture more
informative features and discard information in irrelevant regions.
Furthermore, we propose a multi-prediction guided attention mechanism which
provides pixel-wise semantic prediction guidance to better recover the
segmentation mask at each scale. This mechanism which transforms the deep
supervision to supervised attention is able to guide feature aggregation with
more semantic information between intermediate layers. Experiments on the
publicly available Duke OCT dataset confirm the effectiveness of the proposed
method as well as an improved performance over other state-of-the-art
approaches.
