SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI

2026-06-08

AI Summary

Current assessment of degenerative lumbar spine pathology on MRI largely relies on two-dimensional or subjective grading methods, lacking reliable and reproducible three-dimensional quantitative tools. This work proposes the first open-source, fully automated framework that leverages a robust anatomical segmentation model to jointly analyze three-dimensional morphological and signal characteristics of the spinal canal, spinal cord, vertebrae, intervertebral discs, and neural foramina. The system generates individualized clinical reports contextualized with population-level normative distributions. The method substantially outperforms conventional approaches, achieving an AUC of 0.95 for central canal stenosis (with anteroposterior diameter and area ratios both exceeding 0.80), an AUC of 0.73 for lateral recess stenosis, and shows no significant association for foraminal stenosis. This enables efficient, interpretable, cross-subject and longitudinal quantitative evaluation.

Abstract

Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challenging. In clinical practice, analysis is predominantly performed in two dimensions (2D), as manual three-dimensional (3D) assessment is time-consuming. However, 2D measurements suffer from limited reproducibility, particularly when anatomical structures are not aligned with the imaging plane. Existing automated approaches are often restricted to 2D, rely on discrete grading, or lack robustness and interpretability. We introduce SpineReport, an open-source, fully automated framework for comprehensive 3D morphometric analysis of lumbar spine MRI. Leveraging robust anatomical segmentations, the method extracts quantitative metrics from key structures, including the spinal canal, spinal cord, vertebrae, intervertebral discs, and foramina. These include both morphological and signal-based features, enabling cross-subject and longitudinal assessment. SpineReport further generates subject-specific reports that allow comparison with cohort distributions, improving interpretability and objective characterization of spinal morphology. Clinical relevance was evaluated against radiologist-reported severity grades for central canal, lateral recess, and foraminal stenosis. Metrics showed strong associations with central canal stenosis severity, with T2-weighted CSF signal providing the highest performance (AUC = 0.95). Canal AP diameter and area ratios also demonstrated strong correlations and high discriminative ability (AUC > 0.80). For lateral recess stenosis, associations were moderate, with lateral CSF signal being the most informative (AUC = 0.73). No significant associations were observed for foraminal stenosis despite robust region-of-interest extraction. SpineReport is released as an open-access tool: https://ivadomed.github.io/SpineReport/

Problem

• lumbar spine degeneration
• 3D quantification
• MRI
• automated analysis
• spinal stenosis

Innovation

• 3D morphometric analysis
• automated MRI quantification
• spinal stenosis assessment
• interpretable reporting
• open-source framework