MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images
MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images
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DOI：
10.23919/eusipco47968.2020.9287561
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Zeyu Fu;Yang Sun;X. Zhang;Scott Stainton;Shaun Barney;J. Hogg;W. Innes;S. Dlay
中科院分区：
文献类型：
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作者：
Zeyu Fu;Yang Sun;X. Zhang;Scott Stainton;Shaun Barney;J. Hogg;W. Innes;S. Dlay
光学相干层析成像(OCT)是提取高分辨率视网膜信息的常用方法。此外，人们对视网膜层自动分割的需求也越来越大，这有助于视网膜疾病的诊断。本文提出了一种新的用于OCT图像视网膜层自动分割的多预测引导注意网络(MPG-Net)。该方法包括两个主要步骤，以增强U形全卷积网络(FCN)的识别能力，以实现可靠的自动分割。首先，在编码器中利用特征细化模块自适应地对特征通道重新加权，以获取更多的信息并丢弃不相关区域的信息。此外，我们还提出了一种多预测引导注意机制，该机制提供了像素级的语义预测指导，以更好地恢复每个尺度上的分割掩码。该机制将深度监督转化为监督注意，能够在中间层之间用更多的语义信息来指导特征聚集。在公开可用的Duke OCT数据集上的实验证实了所提出的方法的有效性，并且与其他最先进的方法相比具有更好的性能。
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 multi-prediction 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.