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

• URL: http://arxiv.org/abs/2009.13634v1
• Date: Mon, 28 Sep 2020 21:22:22 GMT
• Title: MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images
• Authors: Zeyu Fu, Yang Sun, Xiangyu Zhang, Scott Stainton, Shaun Barney, Jeffry Hogg, William Innes and Satnam Dlay
• Abstract summary: We propose a novel multiprediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images. MPG-Net consists of two major steps to strengthen the discriminative power of a U-shape Fully convolutional network (FCN) for reliable automated segmentation.
• Score: 11.370735571629602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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