Cited By
View all- Ndipenoch NMiron ALi Y(2024)Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data SourcesIEEE Access10.1109/ACCESS.2024.336991312(31719-31735)Online publication date: 2024
Automatic Layering of Retinal OCT Images with Dual Attention Mechanism
Authors: 
Jun Wu, Jianxu Chen, Zhitao Xiao, Lei GengAuthors Info & Claims
Jun Wu, Jianxu Chen, Zhitao Xiao, Lei GengAuthors Info & ClaimsPages 63 - 69
Abstract
At present, there are more and more people suffering from retinal diseases. Doctors can diagnose and prevent eye diseases by observing the changes in the thickness of the retinal layer in OCT images. Due to the low contrast of the retinal layer boundary of the OCT image, manual segmentation is time-consuming and laborious. Moreover, most of the current automatic retinal layer segmentation methods are based on traditional methods and the segmentation result is not good. Therefore, in this paper, we proposed an end-to-end automatic retinal layer segmentation method based on deep learning, called DA-PSPNet, which can accurately segment seven retinal layers in OCT images. DA-PSPNet integrates a dual attention mechanism based on the PSPNet network, aiming to extract richer layer boundary information. It merges features of various levels to aggregate contextual information in different regions. The experimental results show that the proposed method achieves better performance in several evaluation indexes compared with the other four mainstream segmentation networks.
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Published In
April 2021
168 pages
ISBN:9781450390057
DOI:10.1145/3468945
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Published: 27 September 2021
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- the Program for Innovative Research Team in University of Tianjin
- Major science and technology projects in Tianjin
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IMIP '21
IMIP '21: 2021 3rd International Conference on Intelligent Medicine and Image Processing
April 23 - 26, 2021
Tianjin, China
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View all- Ndipenoch NMiron ALi Y(2024)Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data SourcesIEEE Access10.1109/ACCESS.2024.336991312(31719-31735)Online publication date: 2024
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