Pancreas segmentation algorithm based on depth-wise convolution and tri-orientated spatial attention
10.3969/j.issn.1005-202X.2025.01.006
- VernacularTitle:基于深度卷积和三向注意力感知的胰腺分割算法
- Author:
Lulu TAN
1
;
Qianjin FENG
Author Information
1. 南方医科大学生物医学工程学院,广东广州510515
- Publication Type:Journal Article
- Keywords:
pancreas;
depth-wise convolution;
tri-orientated spatial attention;
cascaded network
- From:
Chinese Journal of Medical Physics
2025;42(1):37-42
- CountryChina
- Language:Chinese
-
Abstract:
A cascaded 3D pancreas segmentation network (CPS-Net) is proposed to address the challenges in pancreas segmentation caused by its small size and complex anatomical structure. CPS-Net is composed of two components:the first part utilizes ResUNet to quickly localize the pancreas region,while the second part uses a network that fuses depth-wise convolution block and tri-orientated spatial attention module to refine the segmentation results. Specifically,depth-wise convolution block significantly enhances the differentiation between the pancreas and surrounding tissues by extracting multi-scale features layer by layer,while tri-orientated spatial attention module combines axial attention,planar attention and window attention mechanisms to comprehensively capture the detailed structure of the pancreas in a complex background. CPS-Net achieved Dice similarity coefficient,positive predictive value,sensitivity,and Hausdorff distance of 87.42％±1.58％,87.42％±3.52％,87.74％±4.58％,and (0.22±0.08) mm,respectively,on the NIH public dataset,demonstrating its higher pancreas segmentation accuracy and superior performance compared with the current state-of-the-art segmentation networks.
