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SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer
Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical i...
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/PMC9221215/ https://www.ncbi.nlm.nih.gov/pubmed/35741682 http://dx.doi.org/10.3390/brainsci12060797 |
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author | Jiang, Yun Zhang, Yuan Lin, Xin Dong, Jinkun Cheng, Tongtong Liang, Jing |
author_facet | Jiang, Yun Zhang, Yuan Lin, Xin Dong, Jinkun Cheng, Tongtong Liang, Jing |
author_sort | Jiang, Yun |
collection | PubMed |
description | Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical image tasks, deep convolutional neural networks based on an encoder–decoder structure and skip-connection have been frequently used. However, CNNs have the drawback of being unable to learn global and remote semantic information well. On the other hand, the transformer has recently found success in natural language processing and computer vision as a result of its usage of a self-attention mechanism for global information modeling. For demanding prediction tasks, such as 3D medical picture segmentation, local and global characteristics are critical. We propose SwinBTS, a new 3D medical picture segmentation approach, which combines a transformer, convolutional neural network, and encoder–decoder structure to define the 3D brain tumor semantic segmentation job as a sequence-to-sequence prediction challenge in this research. To extract contextual data, the 3D Swin Transformer is utilized as the network’s encoder and decoder, and convolutional operations are employed for upsampling and downsampling. Finally, we achieve segmentation results using an improved Transformer module that we built for increasing detail feature extraction. Extensive experimental results on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets reveal that SwinBTS outperforms state-of-the-art 3D algorithms for brain tumor segmentation on 3D MRI scanned images. |
format | Online Article Text |
id | pubmed-9221215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92212152022-06-24 SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer Jiang, Yun Zhang, Yuan Lin, Xin Dong, Jinkun Cheng, Tongtong Liang, Jing Brain Sci Article Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical image tasks, deep convolutional neural networks based on an encoder–decoder structure and skip-connection have been frequently used. However, CNNs have the drawback of being unable to learn global and remote semantic information well. On the other hand, the transformer has recently found success in natural language processing and computer vision as a result of its usage of a self-attention mechanism for global information modeling. For demanding prediction tasks, such as 3D medical picture segmentation, local and global characteristics are critical. We propose SwinBTS, a new 3D medical picture segmentation approach, which combines a transformer, convolutional neural network, and encoder–decoder structure to define the 3D brain tumor semantic segmentation job as a sequence-to-sequence prediction challenge in this research. To extract contextual data, the 3D Swin Transformer is utilized as the network’s encoder and decoder, and convolutional operations are employed for upsampling and downsampling. Finally, we achieve segmentation results using an improved Transformer module that we built for increasing detail feature extraction. Extensive experimental results on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets reveal that SwinBTS outperforms state-of-the-art 3D algorithms for brain tumor segmentation on 3D MRI scanned images. MDPI 2022-06-17 /pmc/articles/PMC9221215/ /pubmed/35741682 http://dx.doi.org/10.3390/brainsci12060797 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 Jiang, Yun Zhang, Yuan Lin, Xin Dong, Jinkun Cheng, Tongtong Liang, Jing SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer |
title | SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer |
title_full | SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer |
title_fullStr | SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer |
title_full_unstemmed | SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer |
title_short | SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer |
title_sort | swinbts: a method for 3d multimodal brain tumor segmentation using swin transformer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221215/ https://www.ncbi.nlm.nih.gov/pubmed/35741682 http://dx.doi.org/10.3390/brainsci12060797 |
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