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Efficient BFCN for Automatic Retinal Vessel Segmentation
Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cann...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803293/ https://www.ncbi.nlm.nih.gov/pubmed/33489334 http://dx.doi.org/10.1155/2020/6439407 |
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author | Jiang, Yun Wang, Falin Gao, Jing Liu, Wenhuan |
author_facet | Jiang, Yun Wang, Falin Gao, Jing Liu, Wenhuan |
author_sort | Jiang, Yun |
collection | PubMed |
description | Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively. |
format | Online Article Text |
id | pubmed-7803293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-78032932021-01-22 Efficient BFCN for Automatic Retinal Vessel Segmentation Jiang, Yun Wang, Falin Gao, Jing Liu, Wenhuan J Ophthalmol Research Article Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively. Hindawi 2020-09-17 /pmc/articles/PMC7803293/ /pubmed/33489334 http://dx.doi.org/10.1155/2020/6439407 Text en Copyright © 2020 Yun Jiang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Jiang, Yun Wang, Falin Gao, Jing Liu, Wenhuan Efficient BFCN for Automatic Retinal Vessel Segmentation |
title | Efficient BFCN for Automatic Retinal Vessel Segmentation |
title_full | Efficient BFCN for Automatic Retinal Vessel Segmentation |
title_fullStr | Efficient BFCN for Automatic Retinal Vessel Segmentation |
title_full_unstemmed | Efficient BFCN for Automatic Retinal Vessel Segmentation |
title_short | Efficient BFCN for Automatic Retinal Vessel Segmentation |
title_sort | efficient bfcn for automatic retinal vessel segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803293/ https://www.ncbi.nlm.nih.gov/pubmed/33489334 http://dx.doi.org/10.1155/2020/6439407 |
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