Improved MambaUNet for lightweight cascaded segmentation in liver tumor CT image(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第8期
- Page:
- 1068-1078
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Improved MambaUNet for lightweight cascaded segmentation in liver tumor CT image
- Author(s):
- LI Ke1; LIU Wenzhong1; 2; QIN Jingtao1
- 1. School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644002, China 2. Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Yibin 644002, China
- Keywords:
- Keywords: liver tumor segmentation MD-MambaUNet CT image Dice similarity coefficient
- PACS:
- R318；TP391.4
- DOI:
- DOI:10.3969/j.issn.1005-202X.2025.08.014
- Abstract:
- Abstract: To address the limitations of convolutional neural networks in global context modeling and the complexity of secondary computation in Transformers self attention mechanism, an improved multi-direction-MambaUNet (MD-MambaUNet) based on MambaUNet is proposed. This network integrates with multi directional selective scanning module to extract spatial features from multiple directions of CT images, significantly improving the global context modeling capability. By introducing partial convolution and constructing a lightweight hybrid convolution module, the models parameter count is significantly reduced, enabling the processing of large-scale medical image data at lower computational costs while maintaining high-level performance. Experiments are conducted on the LiTS2017 and 3DIRCADB public datasets. Compared with MambaUNet on the LiTS2017 dataset, MD-MambaUNet improves the Dice similarity coefficient for liver and tumor segmentation by 3.32% and 4.77%, reaching 95.36% and 76.93%, and increases intersection over union by 4.18% and 4.92%, reaching 91.43% and 69.74%, respectively. Compared with MambaUNet on the 3DIRCADB dataset, MD-MambaUNet improves the Dice similarity coefficient for liver and tumor segmentation by 2.08% and 2.21%, reaching 93.81% and 64.68%, and increases intersection over union by 0.79% and 3.13%, reaching 87.23% and 57.65%, respectively. Meanwhile, the parameter count is 13.71 M less than MambaUNet, making it possible for the model to be deployed in clinical scenarios.
Last Update: 2025-09-15