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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...

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Autores principales: Jiang, Yun, Zhang, Yuan, Lin, Xin, Dong, Jinkun, Cheng, Tongtong, Liang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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