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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9504252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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