Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model
The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, resea...
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MDPI AG
2025-05-01
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| author | Shuli Xing Zhenwei Lai Junxiong Zhu Wenwu He Guojun Mao |
| author_facet | Shuli Xing Zhenwei Lai Junxiong Zhu Wenwu He Guojun Mao |
| author_sort | Shuli Xing |
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| description | The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, researchers are trying to develop an automated and accurate segmentation model. Currently, many segmentation models in deep learning rely on Convolutional Neural Network or Vision Transformer. However, Convolution-based models often fail to deliver precise segmentation results, while Transformer-based models often require more computational resources. To address these challenges, we propose a novel hybrid model named Local–Global UNet Transformer. In our model, we introduce: (1) a semantic-oriented masked attention to enhance the feature extraction capability of the decoder; and (2) network-in-network blocks to increase channel modeling complexity in the encoder while reducing the parameter consumption associated with residual blocks. We evaluate our model on two public brain tumor segmentation datasets, and the experimental results demonstrate that our model achieves the highest average Dice score on the BraTS2024-GLI dataset and ranks second on the BraTS2023-GLI dataset. In terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>H</mi><msub><mi>D</mi><mn>95</mn></msub></mrow></semantics></math></inline-formula>, our model attains the lowest values on both datasets. Furthermore, the ablation study proves the effectiveness of our model design. |
| format | Article |
| id | doaj-art-e79226962ca144eeadcedd3509cda0e9 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e79226962ca144eeadcedd3509cda0e92025-08-20T02:32:34ZengMDPI AGApplied Sciences2076-34172025-05-011511598110.3390/app15115981Semantic Segmentation of Brain Tumors Using a Local–Global Attention ModelShuli Xing0Zhenwei Lai1Junxiong Zhu2Wenwu He3Guojun Mao4College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaThe distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, researchers are trying to develop an automated and accurate segmentation model. Currently, many segmentation models in deep learning rely on Convolutional Neural Network or Vision Transformer. However, Convolution-based models often fail to deliver precise segmentation results, while Transformer-based models often require more computational resources. To address these challenges, we propose a novel hybrid model named Local–Global UNet Transformer. In our model, we introduce: (1) a semantic-oriented masked attention to enhance the feature extraction capability of the decoder; and (2) network-in-network blocks to increase channel modeling complexity in the encoder while reducing the parameter consumption associated with residual blocks. We evaluate our model on two public brain tumor segmentation datasets, and the experimental results demonstrate that our model achieves the highest average Dice score on the BraTS2024-GLI dataset and ranks second on the BraTS2023-GLI dataset. In terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>H</mi><msub><mi>D</mi><mn>95</mn></msub></mrow></semantics></math></inline-formula>, our model attains the lowest values on both datasets. Furthermore, the ablation study proves the effectiveness of our model design.https://www.mdpi.com/2076-3417/15/11/5981braintumorsemantic segmentationhybrid modelchannel modeling |
| spellingShingle | Shuli Xing Zhenwei Lai Junxiong Zhu Wenwu He Guojun Mao Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model Applied Sciences braintumor semantic segmentation hybrid model channel modeling |
| title | Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model |
| title_full | Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model |
| title_fullStr | Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model |
| title_full_unstemmed | Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model |
| title_short | Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model |
| title_sort | semantic segmentation of brain tumors using a local global attention model |
| topic | braintumor semantic segmentation hybrid model channel modeling |
| url | https://www.mdpi.com/2076-3417/15/11/5981 |
| work_keys_str_mv | AT shulixing semanticsegmentationofbraintumorsusingalocalglobalattentionmodel AT zhenweilai semanticsegmentationofbraintumorsusingalocalglobalattentionmodel AT junxiongzhu semanticsegmentationofbraintumorsusingalocalglobalattentionmodel AT wenwuhe semanticsegmentationofbraintumorsusingalocalglobalattentionmodel AT guojunmao semanticsegmentationofbraintumorsusingalocalglobalattentionmodel |