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TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation
Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convol...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223195/ https://www.ncbi.nlm.nih.gov/pubmed/37430810 http://dx.doi.org/10.3390/s23104897 |
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author | Shi, Zidi Li, Yu Zou, Hua Zhang, Xuedong |
author_facet | Shi, Zidi Li, Yu Zou, Hua Zhang, Xuedong |
author_sort | Shi, Zidi |
collection | PubMed |
description | Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convolutional networks. We propose a novel end-to-end transformer-based network architecture called TCU-Net for OCTA retinal vessel segmentation tasks. To address the loss of vascular features of convolutional operations, an efficient cross-fusion transformer module is introduced to replace the original skip connection of U-Net. The transformer module interacts with the encoder’s multiscale vascular features to enrich vascular information and achieve linear computational complexity. Additionally, we design an efficient channel-wise cross attention module to fuse the multiscale features and fine-grained details from the decoding stages, resolving the semantic bias between them and enhancing effective vascular information. This model has been evaluated on the dedicated Retinal OCTA Segmentation (ROSE) dataset. The accuracy values of TCU-Net tested on the ROSE-1 dataset with SVC, DVC, and SVC+DVC are 0.9230, 0.9912, and 0.9042, respectively, and the corresponding AUC values are 0.9512, 0.9823, and 0.9170. For the ROSE-2 dataset, the accuracy and AUC are 0.9454 and 0.8623, respectively. The experiments demonstrate that TCU-Net outperforms state-of-the-art approaches regarding vessel segmentation performance and robustness. |
format | Online Article Text |
id | pubmed-10223195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102231952023-05-28 TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation Shi, Zidi Li, Yu Zou, Hua Zhang, Xuedong Sensors (Basel) Article Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convolutional networks. We propose a novel end-to-end transformer-based network architecture called TCU-Net for OCTA retinal vessel segmentation tasks. To address the loss of vascular features of convolutional operations, an efficient cross-fusion transformer module is introduced to replace the original skip connection of U-Net. The transformer module interacts with the encoder’s multiscale vascular features to enrich vascular information and achieve linear computational complexity. Additionally, we design an efficient channel-wise cross attention module to fuse the multiscale features and fine-grained details from the decoding stages, resolving the semantic bias between them and enhancing effective vascular information. This model has been evaluated on the dedicated Retinal OCTA Segmentation (ROSE) dataset. The accuracy values of TCU-Net tested on the ROSE-1 dataset with SVC, DVC, and SVC+DVC are 0.9230, 0.9912, and 0.9042, respectively, and the corresponding AUC values are 0.9512, 0.9823, and 0.9170. For the ROSE-2 dataset, the accuracy and AUC are 0.9454 and 0.8623, respectively. The experiments demonstrate that TCU-Net outperforms state-of-the-art approaches regarding vessel segmentation performance and robustness. MDPI 2023-05-19 /pmc/articles/PMC10223195/ /pubmed/37430810 http://dx.doi.org/10.3390/s23104897 Text en © 2023 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 Shi, Zidi Li, Yu Zou, Hua Zhang, Xuedong TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation |
title | TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation |
title_full | TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation |
title_fullStr | TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation |
title_full_unstemmed | TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation |
title_short | TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation |
title_sort | tcu-net: transformer embedded in convolutional u-shaped network for retinal vessel segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223195/ https://www.ncbi.nlm.nih.gov/pubmed/37430810 http://dx.doi.org/10.3390/s23104897 |
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