A Hybrid Artificial Intelligence Framework for Lumbar Spine Anatomical Landmark Measurement Extraction from MRI Axial and Sagittal Scans

Lumbar spinal disorders such as stenosis and degeneration are common and debilitating. While lumbar spine MRI is critical for diagnosis, conventional assessments rely on subjective visual grading. To overcome this, we propose a deep learning-based segmentation framework that automatically extracts anatomical landmarks and measures clinically relevant indicators from both sagittal and axial MRI. Our geometry-guided pipeline quantifies vertebral and disc heights, foraminal distances, and spinal canal dimensions with explainable outputs. The system achieved high agreement with expert measurements (MAE $<1.3 ~\text{mm}, \mathrm{r}=0.98$), and outlier filtering improved overall coverage to over 94 %. Designed for transparency and adaptability, the framework offers potential for application to other spinal conditions such as scoliosis and postoperative instability. This work moves toward trustworthy, quantitative AI tools for clinical spinal analysis.