自动量化腰椎退变，3D MRI分析让诊断更准更省时。
SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI
- 基于3D分割自动提取椎管、椎间盘等结构的形态与信号特征。
- 对中央椎管狭窄评估准确率高达AUC 0.95，优于传统2D方法。
- 生成个性化报告，适合临床医生和研究者做客观对比分析。
腰椎疾病是全球致残主因之一，但基于MRI的退变量化仍具挑战。临床多依赖二维（2D）评估，而3D手动分析耗时，且2D测量易受解剖结构偏移影响，重复性差。现有自动化方法常局限于2D、依赖离散分级或缺乏鲁棒性与可解释性。本文提出SpineReport——一个开源的全自动3D形态计量框架，通过稳健的解剖结构分割，从脊髓、椎管、椎体、椎间盘及椎间孔等关键部位提取包括形态与信号特征在内的定量指标，支持跨被试与纵向评估。该框架还生成个体化报告，可与队列数据分布对比，提升结果可解释性。临床验证显示，其指标与放射科医师评估的中央椎管狭窄严重程度高度相关，尤其T2加权脑脊液信号表现最优（AUC = 0.95），椎管前后径与面积比也具有强区分能力（AUC > 0.80）。对于侧隐窝狭窄，关联中等，侧向脑脊液信号最有效（AUC = 0.73）。尽管椎间孔区域分割稳健，但无显著关联。SpineReport已开放获取：https://ivadomed.github.io/SpineReport/
原文摘要 · Abstract (English)
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/
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