App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 6

3d unext: lightweight and efficient network for effective brain extraction

Shen Hualei1,2,3
Wang Qi1
Shangguan Guoqing1
Liu Dong1,2,3
1. School of Computer & Information Engineering, Henan Normal University, Xinxiang 453007, China
2. Key Laboratory of Artificial Intelligence & Personalized Learning in Education of Henan Province, Xinxiang 453007, China
3. Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality of Henan Province, Xinxiang 453007, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0405

Abstract

In order to solve the drawbacks of existing brain extraction network, i. e. , complex network structure, large amounts of parameters and low inference speed, this paper proposed a novel network 3D UNeXt for fast and effective brain extraction. 3D UNeXt greatly reduced parameters and the number of floating point operators (FLOPs) and achieved promising results with the combination of 3D convolution, 3D multilayer perception (MLP) and multi-scale feature fusion. 3D UNeXt used U-Net as the basic architecture and employed 3D convolutional modules to obtain local features in encoding stage. Specifically, the proposed 3D MLP module within the bottleneck enhanced the extraction of global features and long-range dependencies among them. In decoding stage, this paper designed a lightweight multiscale feature fusion module to effectively fuse multiscale low-level features and high-level counterparts. In detail, the 3D MLP module performed linear shift operations in three different axes to obtain global receptive fields from different dimensions and establish long-range dependencies among them. We evaluated 3D UNeXt on three datasets: IBSR, NFBS, and HTU-BrainMask with other counterparts. Experimental results show that the 3D UNeXt is superior over other baselines in terms of network parameters, FLOPs, inference accuracy, and inference speed.

Foundation Support

国家自然科学基金资助项目(62072160)
河南省科技攻关项目(232102211024)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0405
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 6

Publish History

[2024-02-01] Accepted Paper

Cite This Article

申华磊, 王琦, 上官国庆, 等. 3DUNeXt:轻量级快速脑提取网络 [J]. 计算机应用研究, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0405. (Shen Hualei, Wang Qi, Shangguan Guoqing, et al. 3d unext: lightweight and efficient network for effective brain extraction [J]. Application Research of Computers, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0405. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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