Paper Info
Title
Mpg-Net: Multi-Prediction Guided Network For Segmentation Of Retinal Layers In Oct Images
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 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.
Year
DOI
Venue
2020
10.23919/Eusipco47968.2020.9287561
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)
Keywords
DocType
ISSN
Optical coherence tomography (OCT), retinal layer segmentation, fully convolutional network (FCN), self attention, semantic prediction
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Zeyu Fu193.62
Yang Sun24615.21
Xiangyu Zhang300.34
Scott Stainton400.34
Shaun Barney500.34
Jeffry Hogg600.34
William Innes700.34
S. S. Dlay819823.94