Brain Glioma Image Segmentation Based on Convolution and Deformable Attention

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Brain Glioma Image Segmentation Based on Convolution and Deformable Attention

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[1]GAO Yufei,MA Zixing,XU Jing,et al.Brain Glioma Image Segmentation Based on Convolution and Deformable Attention[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):27-32.[doi:10.13705/j.issn.1671-6833.2023.05.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 02 Page number: 27-32 Column: Public date: 2024-03-06

Title:: Brain Glioma Image Segmentation Based on Convolution and Deformable Attention

Author(s):: GAO Yufei; MA Zixing; XU Jing; ZHAO Guohua; SHI Lei; 1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China; 2. Songshan Laboratory, Zhengzhou 450052, China; 3. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 4. The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450003, China

Keywords:: deep learning; brain glioma image segmentation; CNN; Transformer; self-attention mechanism

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2023.05.007

Abstract:: For medical image segmentation tasks such as glioma image segmentation with dense prediction, both local and global dependencies were indispensable. To address the problems that convolutional neural networks lacked the ability to establish global dependencies and the self-attention mechanism had insufficient ability to capture local details, a convolutional and deformable attention-based method for glioma image segmentation was proposed. A serial combination module of convolution and deformable attention Transformer was designed, in which convolution was used to extract local features and the immediately following deformable attention. Transformer was used to capture global dependencies to the establishment of local and global dependencies at different resolutions. As a hybrid CNN-Transformer architecture, the method could achieve accurate brain glioma image segmentation without any pretraining. Experiments showed that the average dice score and the average 95% Hausdorff distance on the BraTS2020 glioma image segmentation dataset were 83. 56% and 11. 30 mm, respectively, achieving comparable segmentation accuracy compared with other methods, while reducing the computational overhead by at least 50% and effectively improving the efficiency of glioma image segmentation.

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Last Update: 2024-03-08