Global and contextual dual attention U-Net model for segmenting thoracic and lumbar vertebrae in spinal sagittal X-ray images
10.13929/j.issn.1003-3289.2025.01.027
- VernacularTitle:基于全局与上下文双注意力U-Net网络于脊柱矢状位X线片中分割胸椎及腰椎
- Author:
Lin XIAO
1
;
Li ZHANG
;
Yu TANG
;
Yuyao HUANG
;
Lihang WANG
;
Li HE
;
Zhiqin HE
Author Information
1. 贵州大学电气工程学院,贵州贵阳 550025
- Publication Type:Journal Article
- Keywords:
thoracic vertebrae;
lumbar vertebrae;
X-rays;
artificial intelligence;
automatic segmentation
- From:
Chinese Journal of Medical Imaging Technology
2025;41(1):128-132
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of global and contextual dual attention U-Net model for segmenting thoracic and lumbar vertebrae in spinal sagittal X-ray images.Methods Totally 600 spinal sagittal X-ray images of 600 patients with adolescent idiopathic scoliosis were retrospectively enrolled.The images were preprocessed,and T4-T12 and L1-L5 were manually annotated as reference standards.The global attention refinement(GAR)module and attention-based atrous spatial pyramid pooling(A-ASPP)module were added to U-Net model,fivefold cross validation method was used for training and validation,and its performance for segmenting sagittal X-ray images was analyzed,and compared with pyramid scene parsing network(PSPNet),visual geometry group(VGG)-UNet and DeepLabv3+.Results The precision,sensitivity and Dice similarity coefficient of global and contextual dual attention U-Net model for segmenting thoracic and lumbar vertebrae in spinal sagittal X-ray images was 90.58％,89.51％,and 90.20％,respectively,which were superior to PSPNet,VGG-UNet and DeepLabv3+.The loss function and mean intersection over union curves showed that it converged quickly and had good generalization ability.Conclusion The global and contextual dual attention U-Net model could effectively segment thoracic and lumbar vertebrae in spinal sagittal X-ray images.
