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3D bi-directional transformer U-Net for medical image segmentation
As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within i...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853518/ https://www.ncbi.nlm.nih.gov/pubmed/36687770 http://dx.doi.org/10.3389/fdata.2022.1080715 |
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author | Fu, Xiyao Sun, Zhexian Tang, Haoteng Zou, Eric M. Huang, Heng Wang, Yong Zhan, Liang |
author_facet | Fu, Xiyao Sun, Zhexian Tang, Haoteng Zou, Eric M. Huang, Heng Wang, Yong Zhan, Liang |
author_sort | Fu, Xiyao |
collection | PubMed |
description | As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics. |
format | Online Article Text |
id | pubmed-9853518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98535182023-01-21 3D bi-directional transformer U-Net for medical image segmentation Fu, Xiyao Sun, Zhexian Tang, Haoteng Zou, Eric M. Huang, Heng Wang, Yong Zhan, Liang Front Big Data Big Data As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics. Frontiers Media S.A. 2023-01-06 /pmc/articles/PMC9853518/ /pubmed/36687770 http://dx.doi.org/10.3389/fdata.2022.1080715 Text en Copyright © 2023 Fu, Sun, Tang, Zou, Huang, Wang and Zhan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Fu, Xiyao Sun, Zhexian Tang, Haoteng Zou, Eric M. Huang, Heng Wang, Yong Zhan, Liang 3D bi-directional transformer U-Net for medical image segmentation |
title | 3D bi-directional transformer U-Net for medical image segmentation |
title_full | 3D bi-directional transformer U-Net for medical image segmentation |
title_fullStr | 3D bi-directional transformer U-Net for medical image segmentation |
title_full_unstemmed | 3D bi-directional transformer U-Net for medical image segmentation |
title_short | 3D bi-directional transformer U-Net for medical image segmentation |
title_sort | 3d bi-directional transformer u-net for medical image segmentation |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853518/ https://www.ncbi.nlm.nih.gov/pubmed/36687770 http://dx.doi.org/10.3389/fdata.2022.1080715 |
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