Adaptive cascaded transformer U-Net for MRI brain tumor segmentation
© 2024 Institute of Physics and Engineering in Medicine..
OBJECTIVE: Brain tumor segmentation on magnetic resonance imaging (MRI) plays an important role in assisting the diagnosis and treatment of cancer patients. Recently, cascaded U-Net models have achieved excellent performance via conducting coarse-to-fine segmentation of MRI brain tumors. However, they still suffer from obvious global and local differences among various brain tumors, which are difficult to solve with conventional convolutions.
APPROACH: To address the issue, this work proposes a novel Adaptive Cascaded Transformer U-Net (ACTransU-Net) for MRI brain tumor segmentation, which simultaneously integrates Transformer and dynamic convolution into a single cascaded U-Net architecture to adaptively capture global information and local details of brain tumors. ACTransU-Net first cascades two 3D U-Nets into a two-stage network to segment brain tumors from coarse to fine. Subsequently, it integrates omni-dimensional dynamic convolution modules into the second-stage shallow encoder and decoder, thereby enhancing the local detail representation of various brain tumors through dynamically adjusting convolution kernel parameters. Moreover, 3D Swin-Transformer modules are introduced into the second-stage deep encoder and decoder to capture image long-range dependencies, which helps adapt the global representation of brain tumors.
MAIN RESULTS: Extensive experiment results evaluated on the public BraTS 2020 and BraTS 2021 brain tumor datasets demonstrate the effectiveness of ACTransU-Net, with average DSC of 84.96% and 91.37%, and HD95 of 10.81 mm and 7.31 mm, proving competitiveness with the state-of-the-art methods.
SIGNIFICANCE: The proposed method focuses on adaptively capturing both global information and local details of brain tumors, aiding physicians in their accurate diagnosis. Additionally, it has the potential to extend ACTransU-Net for segmenting other types of lesions. The source code is available at: https://github.com/chenbn266/ACTransUnet.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Physics in medicine and biology - (2024) vom: 18. Apr. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Bonian [VerfasserIn] |
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| Links: |
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| Themen: |
Brain tumor segmentation |
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| Anmerkungen: |
Date Revised 18.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1088/1361-6560/ad4081 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM371254876 |
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| 500 | |a Date Revised 18.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024 Institute of Physics and Engineering in Medicine. | ||
| 520 | |a OBJECTIVE: Brain tumor segmentation on magnetic resonance imaging (MRI) plays an important role in assisting the diagnosis and treatment of cancer patients. Recently, cascaded U-Net models have achieved excellent performance via conducting coarse-to-fine segmentation of MRI brain tumors. However, they still suffer from obvious global and local differences among various brain tumors, which are difficult to solve with conventional convolutions | ||
| 520 | |a APPROACH: To address the issue, this work proposes a novel Adaptive Cascaded Transformer U-Net (ACTransU-Net) for MRI brain tumor segmentation, which simultaneously integrates Transformer and dynamic convolution into a single cascaded U-Net architecture to adaptively capture global information and local details of brain tumors. ACTransU-Net first cascades two 3D U-Nets into a two-stage network to segment brain tumors from coarse to fine. Subsequently, it integrates omni-dimensional dynamic convolution modules into the second-stage shallow encoder and decoder, thereby enhancing the local detail representation of various brain tumors through dynamically adjusting convolution kernel parameters. Moreover, 3D Swin-Transformer modules are introduced into the second-stage deep encoder and decoder to capture image long-range dependencies, which helps adapt the global representation of brain tumors | ||
| 520 | |a MAIN RESULTS: Extensive experiment results evaluated on the public BraTS 2020 and BraTS 2021 brain tumor datasets demonstrate the effectiveness of ACTransU-Net, with average DSC of 84.96% and 91.37%, and HD95 of 10.81 mm and 7.31 mm, proving competitiveness with the state-of-the-art methods | ||
| 520 | |a SIGNIFICANCE: The proposed method focuses on adaptively capturing both global information and local details of brain tumors, aiding physicians in their accurate diagnosis. Additionally, it has the potential to extend ACTransU-Net for segmenting other types of lesions. The source code is available at: https://github.com/chenbn266/ACTransUnet | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Transformer | |
| 650 | 4 | |a U-Net | |
| 650 | 4 | |a brain tumor segmentation | |
| 650 | 4 | |a cascaded network | |
| 650 | 4 | |a dynamic convolution | |
| 650 | 4 | |a magnetic resonance imaging | |
| 700 | 1 | |a Sun, Qiule |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Yutong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jianxin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qiang |e verfasserin |4 aut | |
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