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MRI Brain Tumour Segmentation Using Multiscale Attention U-Net
Volume 35, Issue 4 (2024), pp. 751–774
Pub. online: 30 October 2024      Type: Research Article      Open accessOpen Access
Received
1 March 2024
Accepted
1 October 2024
Published
30 October 2024

Abstract

Focusing on the problems of failing to make full use of spatial context information and limited local receptive field when U-Net is utilized to solve MRI brain tumour segmentation, a novel 3D multi-scale attention U-Net method, i.e. MAU-Net, is proposed in this paper. Firstly, a Mixed Depth-wise Convolution (MDConv) module is introduced in the encoder and decoder, which leverages various convolution kernels to extract the multi-scale features of brain tumour images, and effectively strengthens the feature expression of the brain tumour lesion region in the up and down sampling. Secondly, a Context Pyramid Module (CPM) combining multi-scale and attention is embedded in the skip connection position to achieve the combination of local feature enhancement at multi-scale with global feature correlation. Finally, MAU-Net adopts Self-ensemble in the decoding process to achieve complementary detailed features of sampled brain tumour images at different scales, thereby further improving segmentation performance. Ablation and comparison experiment results on the publicly available BraTS 2019/2020 datasets well validate its effectiveness. It respectively achieves the Dice Similarity Coefficients (DSC) of 90.6%/90.2%, 82.7%/82.8%, and 77.9%/78.5% on the whole tumour (WT), tumour core (TC) and enhanced tumour (ET) segmentation. Additionally, on the BraTS 2021 training set, the DSC for WT, TC, and ET reached 93.7%, 93.2%, and 88.9%, respectively.

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Biographies

Chen Bonian

B. Chen is currently a graduate student at College of Computer Science and Engineering, Dalian Minzu University, Dalian, China. His main research interests include computer vision and medical image analysis.

He Tao

T. He received his master’s degree from College of Computer Science and Engineering, Dalian Minzu University, Dalian, China. His main research interests include computer vision and medical image analysis.

Wang Weizhuo

W.Wang is currently a lecturer at college of International Business, Dalian Minzu University, Dalian, China. She received her PhD degree from Lincoln University, New Zealand. Her main research interests include sustainable finance and financial risk management.

Han Yutong
hanyt@dlnu.edu.cn

Y. Han is currently a lecturer at College of Computer Science and Engineering, Dalian Minzu University, Dalian, China. Her main research interests are database technology and medical image analysis.

Zhang Jianxin
jxzhang@dlnu.edu.cn

J. Zhang is currently a professor at College of Computer Science and Engineering, Dalian Minzu University, Dalian, China. His main research interests include computer vision and intelligent medical data processing.

Bobek Samo

S. Bobek is currently a professor at the Faculty of Economics and Business, Maribor University, Maribor, Slovenia. His main research interests are in E-commerce, business and information systems.

Zabukovsek Simona Sternad

S.S. Zabukovsek is currently a professor at the Faculty of Economics and Business, Maribor University, Maribor, Slovenia. Her main research interests are in financial management and qualitative modelling.


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Copyright
© 2024 Vilnius University
Open access article under the CC BY license.

Keywords
brain tumour segmentation deep learning 3D U-Net multi-scale feature attention mechanism

Funding
This research was funded by the National Natural Science Foundation of China under Grant 61972062, the Applied Basic Research Project of Liaoning under grants 2023JH2/101300191 and 2023JH2/101300193, the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under grant ADIC2023ZD003.

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