Automatic measurement of vertebral compression ratio on lumbar MR images fracture assessment based on MS-Res-AttU-Net model framework.
Authors
Affiliations (1)
Affiliations (1)
- Department of Nursing, The Third People's Hospital of Hefei, Hefei, China.
Abstract
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. 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. 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. 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.