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HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation

Doctors usually diagnose a disease by evaluating the pattern of abnormal blood vessels in the fundus. At present, the segmentation of fundus blood vessels based on deep learning has achieved great success, but it still faces the problems of low accuracy and capillary rupture. A good vessel segmentat...

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Detalles Bibliográficos
Autores principales: Hu, Xiaolong, Wang, Liejun, Li, Yongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504252/
https://www.ncbi.nlm.nih.gov/pubmed/36146132
http://dx.doi.org/10.3390/s22186782
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author Hu, Xiaolong
Wang, Liejun
Li, Yongming
author_facet Hu, Xiaolong
Wang, Liejun
Li, Yongming
author_sort Hu, Xiaolong
collection PubMed
description Doctors usually diagnose a disease by evaluating the pattern of abnormal blood vessels in the fundus. At present, the segmentation of fundus blood vessels based on deep learning has achieved great success, but it still faces the problems of low accuracy and capillary rupture. A good vessel segmentation method can guide the early diagnosis of eye diseases, so we propose a novel hybrid Transformer network (HT-Net) for fundus imaging analysis. HT-Net can improve the vessel segmentation quality by capturing detailed local information and implementing long-range information interactions, and it mainly consists of the following blocks. The feature fusion block (FFB) is embedded in the shallow levels, and FFB enriches the feature space. In addition, the feature refinement block (FRB) is added to the shallow position of the network, which solves the problem of vessel scale change by fusing multi-scale feature information to improve the accuracy of segmentation. Finally, HT-Net’s bottom-level position can capture remote dependencies by combining the Transformer and CNN. We prove the performance of HT-Net on the DRIVE, CHASE_DB1, and STARE datasets. The experiment shows that FFB and FRB can effectively improve the quality of microvessel segmentation by extracting multi-scale information. Embedding efficient self-attention mechanisms in the network can effectively improve the vessel segmentation accuracy. The HT-Net exceeds most existing methods, indicating that it can perform the task of vessel segmentation competently.
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spelling pubmed-95042522022-09-24 HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation Hu, Xiaolong Wang, Liejun Li, Yongming Sensors (Basel) Article Doctors usually diagnose a disease by evaluating the pattern of abnormal blood vessels in the fundus. At present, the segmentation of fundus blood vessels based on deep learning has achieved great success, but it still faces the problems of low accuracy and capillary rupture. A good vessel segmentation method can guide the early diagnosis of eye diseases, so we propose a novel hybrid Transformer network (HT-Net) for fundus imaging analysis. HT-Net can improve the vessel segmentation quality by capturing detailed local information and implementing long-range information interactions, and it mainly consists of the following blocks. The feature fusion block (FFB) is embedded in the shallow levels, and FFB enriches the feature space. In addition, the feature refinement block (FRB) is added to the shallow position of the network, which solves the problem of vessel scale change by fusing multi-scale feature information to improve the accuracy of segmentation. Finally, HT-Net’s bottom-level position can capture remote dependencies by combining the Transformer and CNN. We prove the performance of HT-Net on the DRIVE, CHASE_DB1, and STARE datasets. The experiment shows that FFB and FRB can effectively improve the quality of microvessel segmentation by extracting multi-scale information. Embedding efficient self-attention mechanisms in the network can effectively improve the vessel segmentation accuracy. The HT-Net exceeds most existing methods, indicating that it can perform the task of vessel segmentation competently. MDPI 2022-09-08 /pmc/articles/PMC9504252/ /pubmed/36146132 http://dx.doi.org/10.3390/s22186782 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hu, Xiaolong
Wang, Liejun
Li, Yongming
HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
title HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
title_full HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
title_fullStr HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
title_full_unstemmed HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
title_short HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
title_sort ht-net: a hybrid transformer network for fundus vessel segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504252/
https://www.ncbi.nlm.nih.gov/pubmed/36146132
http://dx.doi.org/10.3390/s22186782
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