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A transformer-based generative adversarial network for brain tumor segmentation
Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we propo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750177/ https://www.ncbi.nlm.nih.gov/pubmed/36532274 http://dx.doi.org/10.3389/fnins.2022.1054948 |
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author | Huang, Liqun Zhu, Enjun Chen, Long Wang, Zhaoyang Chai, Senchun Zhang, Baihai |
author_facet | Huang, Liqun Zhu, Enjun Chen, Long Wang, Zhaoyang Chai, Senchun Zhang, Baihai |
author_sort | Huang, Liqun |
collection | PubMed |
description | Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min–max game progress. The generator is based on a typical “U-shaped” encoder–decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale L(1) loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted exclusive experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods. On additional datasets, including BRATS2018 and BRATS2020, experimental results prove that our technique is capable of generalizing successfully. |
format | Online Article Text |
id | pubmed-9750177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97501772022-12-15 A transformer-based generative adversarial network for brain tumor segmentation Huang, Liqun Zhu, Enjun Chen, Long Wang, Zhaoyang Chai, Senchun Zhang, Baihai Front Neurosci Neuroscience Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min–max game progress. The generator is based on a typical “U-shaped” encoder–decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale L(1) loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted exclusive experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods. On additional datasets, including BRATS2018 and BRATS2020, experimental results prove that our technique is capable of generalizing successfully. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9750177/ /pubmed/36532274 http://dx.doi.org/10.3389/fnins.2022.1054948 Text en Copyright © 2022 Huang, Zhu, Chen, Wang, Chai and Zhang. 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 | Neuroscience Huang, Liqun Zhu, Enjun Chen, Long Wang, Zhaoyang Chai, Senchun Zhang, Baihai A transformer-based generative adversarial network for brain tumor segmentation |
title | A transformer-based generative adversarial network for brain tumor segmentation |
title_full | A transformer-based generative adversarial network for brain tumor segmentation |
title_fullStr | A transformer-based generative adversarial network for brain tumor segmentation |
title_full_unstemmed | A transformer-based generative adversarial network for brain tumor segmentation |
title_short | A transformer-based generative adversarial network for brain tumor segmentation |
title_sort | transformer-based generative adversarial network for brain tumor segmentation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750177/ https://www.ncbi.nlm.nih.gov/pubmed/36532274 http://dx.doi.org/10.3389/fnins.2022.1054948 |
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