MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.

Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial featu...

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Autores principales:	Yun Jiang, Chao Wu, Ge Wang, Hui-Xia Yao, Wen-Huan Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3ce867d7eca64fdb94012dbc73d4a885
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spelling	oai:doaj.org-article:3ce867d7eca64fdb94012dbc73d4a8852021-12-02T20:05:05ZMFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.1932-620310.1371/journal.pone.0253056https://doaj.org/article/3ce867d7eca64fdb94012dbc73d4a8852021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253056https://doaj.org/toc/1932-6203Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results. At the same time, the "layer-by-layer" information fusion between encoder and decoder makes the feature information extracted from the shallow layer of the network cannot be smoothly transferred to the deep layer of the network, resulting in noise in the segmentation features. In this paper, we propose the MFI-Net (Multi-resolution fusion input network) network model to alleviate the above problem to a certain extent. The multi-resolution input module in MFI-Net avoids the loss of coarse-grained feature information in the shallow layer by extracting local and global feature information in different resolutions. We have reconsidered the information fusion method between the encoder and the decoder, and used the information aggregation method to alleviate the information isolation between the shallow and deep layers of the network. MFI-Net is verified on three datasets, DRIVE, CHASE_DB1 and STARE. The experimental results show that our network is at a high level in several metrics, with F1 higher than U-Net by 2.42%, 2.46% and 1.61%, higher than R2U-Net by 1.47%, 2.22% and 0.08%, respectively. Finally, this paper proves the robustness of MFI-Net through experiments and discussions on the stability and generalization ability of MFI-Net.Yun JiangChao WuGe WangHui-Xia YaoWen-Huan LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253056 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yun Jiang Chao Wu Ge Wang Hui-Xia Yao Wen-Huan Liu MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
description	Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results. At the same time, the "layer-by-layer" information fusion between encoder and decoder makes the feature information extracted from the shallow layer of the network cannot be smoothly transferred to the deep layer of the network, resulting in noise in the segmentation features. In this paper, we propose the MFI-Net (Multi-resolution fusion input network) network model to alleviate the above problem to a certain extent. The multi-resolution input module in MFI-Net avoids the loss of coarse-grained feature information in the shallow layer by extracting local and global feature information in different resolutions. We have reconsidered the information fusion method between the encoder and the decoder, and used the information aggregation method to alleviate the information isolation between the shallow and deep layers of the network. MFI-Net is verified on three datasets, DRIVE, CHASE_DB1 and STARE. The experimental results show that our network is at a high level in several metrics, with F1 higher than U-Net by 2.42%, 2.46% and 1.61%, higher than R2U-Net by 1.47%, 2.22% and 0.08%, respectively. Finally, this paper proves the robustness of MFI-Net through experiments and discussions on the stability and generalization ability of MFI-Net.
format	article
author	Yun Jiang Chao Wu Ge Wang Hui-Xia Yao Wen-Huan Liu
author_facet	Yun Jiang Chao Wu Ge Wang Hui-Xia Yao Wen-Huan Liu
author_sort	Yun Jiang
title	MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
title_short	MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
title_full	MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
title_fullStr	MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
title_full_unstemmed	MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.
title_sort	mfi-net: a multi-resolution fusion input network for retinal vessel segmentation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3ce867d7eca64fdb94012dbc73d4a885
work_keys_str_mv	AT yunjiang mfinetamultiresolutionfusioninputnetworkforretinalvesselsegmentation AT chaowu mfinetamultiresolutionfusioninputnetworkforretinalvesselsegmentation AT gewang mfinetamultiresolutionfusioninputnetworkforretinalvesselsegmentation AT huixiayao mfinetamultiresolutionfusioninputnetworkforretinalvesselsegmentation AT wenhuanliu mfinetamultiresolutionfusioninputnetworkforretinalvesselsegmentation
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MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation.

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