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MDMU-Net: 3D multi-dimensional decoupled multi-scale U-Net for pancreatic cancer segmentation
Lian Lu1,2, Miao Wu1,2, Gan Sen1,2
1College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Peerj. Computer Science
|September 24, 2025
Summary
A new lightweight 3D segmentation algorithm, Multi-Dimensional Decoupled Multi-Scale U-Net (MDMU-Net), improves pancreatic cancer diagnosis. It enhances segmentation accuracy and computational efficiency for better treatment planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Pancreatic cancer is a lethal malignancy with diagnostic and treatment challenges.
- Accurate segmentation of pancreas and tumors in CT scans is vital but difficult due to image complexities.
- Manual segmentation is time-consuming and requires expert experience.
Purpose of the Study:
- To develop a lightweight, automated 3D segmentation algorithm for pancreatic cancer.
- To improve the accuracy and efficiency of pancreatic and tumor segmentation in CT images.
- To provide algorithmic support for precise diagnosis and treatment of pancreatic cancer.
Main Methods:
- Proposed a novel algorithm: Multi-Dimensional Decoupled Multi-Scale U-Net (MDMU-Net).
- Utilized depthwise separable convolution to reduce model complexity.
- Incorporated a multi-dimensional decoupled multi-scale module and cross-dimensional attention mechanisms.
Main Results:
- MDMU-Net achieved competitive pancreatic segmentation DSC (0.7108/0.7709) and improved tumor segmentation DSC by 11.8% over AttentionUNet.
- Demonstrated a 15.3% enhancement in HD95 boundary accuracy compared to 3DUX-Net.
- Significantly reduced parameters (65.5%) and FLOPs (71%) compared to UNETR, enhancing computational efficiency.
Conclusions:
- MDMU-Net offers a computationally efficient and accurate solution for pancreatic cancer segmentation.
- The algorithm shows potential for precise diagnosis and treatment planning in pancreatic cancer.
- MDMU-Net provides a reliable algorithmic foundation for clinical applications.
