Automatic measurement of vertebral compression ratio on lumbar MR images fracture assessment based on MS-Res-AttU-Net model framework
Jin Xue, Rao Yu, Lifei Wang, Ping ZhaoAbstract
Objectives
To develop an MS-Res-AttU-Net-based deep learning framework for automatic measurement of vertebral compression ratio (VCR) on lumbar magnetic resonance images and to evaluate its value for image-based assessment of lumbar vertebral fractures.
Methods
This retrospective study included 92 patients with lumbar vertebral fractures who underwent sagittal T2-weighted MRI. An MS-Res-AttU-Net framework was constructed for vertebral segmentation and automatic VCR calculation. The dataset was divided into a training cohort (n=64) and an independent test cohort (n=28). Segmentation performance was assessed using sensitivity, specificity, accuracy, and Dice similarity coefficient. Agreement between automated and manual VCR measurements was evaluated using correlation, intraclass correlation coefficient, and Bland–Altman analysis. An ablation study was further performed to assess the contribution of residual, attention, and multi-scale refinement modules.
Results
The final MS-Res-AttU-Net achieved stable segmentation performance and showed close agreement between automated and manual VCR measurements. The ablation study demonstrated progressive improvement in both segmentation quality and downstream VCR estimation, while A qualitative comparison of four lumbar MR cases showed MS-Res-AttU-Net produced the smoothest and most accurate vertebral contours.
Conclusions
Automatic VCR measurement on lumbar MR images is feasible and clinically interpretable. The MS-Res-AttU-Net-based framework may provide a rapid and objective quantitative tool for lumbar fracture evaluation.