High-Order Interaction and Low-Order Parallelization of Features Fusion With Novel Mamba-UNet Architecture for Medical Image Segmentation

Qianhang Du; Zhenyu Lei; Jiujun Cheng; Masaaki Omura; Hideyuki Hasegawa; Shangce Gao

doi:10.1109/JAS.2026.125720

Volume 13 Issue 6

Jun. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(6): 1378-1391

Q. Du, Z. Lei, J. Cheng, M. Omura, H. Hasegawa, and S. Gao, “High-order interaction and low-order parallelization of features fusion with novel Mamba-UNet architecture for medical image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1378–1391, Jun. 2026. doi: 10.1109/JAS.2026.125720

Citation:

Q. Du, Z. Lei, J. Cheng, M. Omura, H. Hasegawa, and S. Gao, “High-order interaction and low-order parallelization of features fusion with novel Mamba-UNet architecture for medical image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1378–1391, Jun. 2026. doi: 10.1109/JAS.2026.125720

Citation:

Q. Du, Z. Lei, J. Cheng, M. Omura, H. Hasegawa, and S. Gao, “High-order interaction and low-order parallelization of features fusion with novel Mamba-UNet architecture for medical image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1378–1391, Jun. 2026. doi: 10.1109/JAS.2026.125720

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High-Order Interaction and Low-Order Parallelization of Features Fusion With Novel Mamba-UNet Architecture for Medical Image Segmentation

doi: 10.1109/JAS.2026.125720

Funds: This research was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP25K21298, JP25K03179), and Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING) (JPMJSP2145)

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Author Bio:
Qianhang Du received the B.E. degree in data science and big data technology from University of Bengbu in 2023. He is currently pursuing his M.E. degree at University of Toyama, Japan. His current research interests include deep learning, neural networks, medical image segmentation

Zhenyu Lei (Member, IEEE) received the Ph.D. degree in science and engineering from the University of Toyama, Toyama, Japan, in 2023. He is currently an Assistant Professor with the Faculty of Engineering, University of Toyama, Japan. His current research interests include evolutionary computation, machine learning, and neural network for real-world applications and optimization problems

Jiujun Cheng received the Ph.D. degree from the Beijing University of Posts and Telecommunications, in 2006. In 2009, he was a Visiting Professor with Aalto University, Espoo, Finland. He is currently a Professor with Tongji University. He has over 80 publications including conference and journal articles. His current research interests include mobile computing, complex networks, Internet of Vehicles, and autonomous cars

Masaaki Omura (Member, IEEE) received the Ph.D. degree in engineering from Chiba University, Japan, in 2020. While he was a Ph.D. Student, he was supported by the Research Fellowship for Young Scientists (DC1) of the Japan Society for the Promotion of Science (JSPS). From 2020 to 2021, he has been working as a Postdoctoral Researcher with University of Toyama, Japan, granted by the Research Fellowship for Young Scientists (PD) of the JSPS. From 2022 to 2024, he was an Assistant Professor with University of Toyama, Japan. Since 2024, he has been an Associate Professor at University of Toyama, Japan. He is a member of the Acoustical Society of Japan, a Fellow of the Japan Society of Ultrasound in Medicine

Hideyuki Hasegawa (Member, IEEE) received the B.S. degree in control engineering from Nanjing University of Science and Technology in 1983, the M.S. degree in automatic control from Beijing Institute of Technology in 1986, and the Ph.D. degree from Tohoku University, Sendai, Japan, in 2001. He has been a Professor with the Graduate School of Science and Engineering for Research, University of Toyama, Toyama, Japan, since 2015. His research interests are medical ultrasonics. Dr. Hasegawa received the research fellowships for young scientists (DC1). He serves as a Technical Program Committee Member of the IEEE International Ultrasonics Symposium and an Associate Editor for IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Shangce Gao (Senior Member, IEEE) received his Ph.D. degree in Innovative Life Science from the University of Toyama, Toyama, Japan, in 2011. He is currently a Professor with the Faculty of Engineering at the University of Toyama, Japan. His research interests focus on brain-inspired neural networks and their applications in medical diagnosis and drug discovery. He serves as an Associate Editor for several international journals, including IEEE Transactions on Neural Networks and Learning Systems and IEEE/CAA Journal of Automatica Sinica
Corresponding author: Jiujun Cheng, e-mail: chengjj@tongji.edu.cn; Shangce Gao, e-mail: gaosc@eng.u-toyama.ac.jp
Received Date: 2025-06-20
Revised Date: 2025-09-06
Accepted Date: 2025-11-04

Available Online: 2026-06-22

Abstract

Abstract

Medical image segmentation is an essential method for computer-aided diagnosis. Although image segmentation models based on convolutional neural networks (CNNs) and vision transformers (ViTs) have achieved significant advancements, CNNs struggle to effectively capture long-range dependencies, while ViTs face limitations in local information extraction and are hindered by quadratic computational complexity. Recently, their inherent issues have been successfully addressed by the state-space models in Mamba and 2D-selective-scan in Vision Mamba. However, the presence of noise and excessive redundant information in medical images limits the practicality of these methods. To address these challenges, we propose a highly effective and accurate high-low-order feature fusion visual state space module, named HL-VSS. This module primarily consists of two core components: multi-scale spatial convolution and high-low-order feature fusion (HLFF). The former component preliminarily suppresses noise and captures multi-scale feature information from medical images, accurately extracting edge and detail features for the fusion component. The latter processes these features, further reducing redundant information through high-order interaction with 2D-selective-scan, and fuses the local features obtained by low-order parallel Mamba, ultimately extracting deeper medical image features. We incorporate HL-VSS into a U-shaped architecture, named high-low-order feature fusion visual Mamba UNet (V-UNet). Comparison experiments and ablation studies are conducted on four publicly available medical image datasets to validate the strong competitiveness of V-UNet in medical image segmentation tasks. The code is available at https://github.com/ai-dqh0106/V-UNet_Code.
- 2D-selective-scan (SS2D),
- deep learning,
- high-low-order feature fusion (HLFF),
- Mamba,
- medical image segmentation,
- state-space models

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