HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.

The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to t...

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Autores principales:	Xiaolong Hu, Liejun Wang, Shuli Cheng, Yongming Li
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/47022759f5ce458893b6ad4174ac7120
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spelling	oai:doaj.org-article:47022759f5ce458893b6ad4174ac71202021-12-02T20:08:27ZHDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.1932-620310.1371/journal.pone.0257013https://doaj.org/article/47022759f5ce458893b6ad4174ac71202021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257013https://doaj.org/toc/1932-6203The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to the early treatment and initiative prevention of ophthalmic diseases. In our work, we propose a hierarchical dilation convolutional network (HDC-Net) to extract retinal vessels in a pixel-to-pixel manner. It utilizes the hierarchical dilation convolution (HDC) module to capture the fragile retinal blood vessels usually neglected by other methods. An improved residual dual efficient channel attention (RDECA) module can infer more delicate channel information to reinforce the discriminative capability of the model. The structured Dropblock can help our HDC-Net model to solve the network overfitting effectively. From a holistic perspective, the segmentation results obtained by HDC-Net are superior to other deep learning methods on three acknowledged datasets (DRIVE, CHASE-DB1, STARE), the sensitivity, specificity, accuracy, f1-score and AUC score are {0.8252, 0.9829, 0.9692, 0.8239, 0.9871}, {0.8227, 0.9853, 0.9745, 0.8113, 0.9884}, and {0.8369, 0.9866, 0.9751, 0.8385, 0.9913}, respectively. It surpasses most other advanced retinal vessel segmentation models. Qualitative and quantitative analysis demonstrates that HDC-Net can fulfill the task of retinal vessel segmentation efficiently and accurately.Xiaolong HuLiejun WangShuli ChengYongming LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257013 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaolong Hu Liejun Wang Shuli Cheng Yongming Li HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.
description	The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to the early treatment and initiative prevention of ophthalmic diseases. In our work, we propose a hierarchical dilation convolutional network (HDC-Net) to extract retinal vessels in a pixel-to-pixel manner. It utilizes the hierarchical dilation convolution (HDC) module to capture the fragile retinal blood vessels usually neglected by other methods. An improved residual dual efficient channel attention (RDECA) module can infer more delicate channel information to reinforce the discriminative capability of the model. The structured Dropblock can help our HDC-Net model to solve the network overfitting effectively. From a holistic perspective, the segmentation results obtained by HDC-Net are superior to other deep learning methods on three acknowledged datasets (DRIVE, CHASE-DB1, STARE), the sensitivity, specificity, accuracy, f1-score and AUC score are {0.8252, 0.9829, 0.9692, 0.8239, 0.9871}, {0.8227, 0.9853, 0.9745, 0.8113, 0.9884}, and {0.8369, 0.9866, 0.9751, 0.8385, 0.9913}, respectively. It surpasses most other advanced retinal vessel segmentation models. Qualitative and quantitative analysis demonstrates that HDC-Net can fulfill the task of retinal vessel segmentation efficiently and accurately.
format	article
author	Xiaolong Hu Liejun Wang Shuli Cheng Yongming Li
author_facet	Xiaolong Hu Liejun Wang Shuli Cheng Yongming Li
author_sort	Xiaolong Hu
title	HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.
title_short	HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.
title_full	HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.
title_fullStr	HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.
title_full_unstemmed	HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.
title_sort	hdc-net: a hierarchical dilation convolutional network for retinal vessel segmentation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/47022759f5ce458893b6ad4174ac7120
work_keys_str_mv	AT xiaolonghu hdcnetahierarchicaldilationconvolutionalnetworkforretinalvesselsegmentation AT liejunwang hdcnetahierarchicaldilationconvolutionalnetworkforretinalvesselsegmentation AT shulicheng hdcnetahierarchicaldilationconvolutionalnetworkforretinalvesselsegmentation AT yongmingli hdcnetahierarchicaldilationconvolutionalnetworkforretinalvesselsegmentation
_version_	1718375176814460928

HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.

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