Automated multi-sequence MRI quantitative assessment and 3D visualization of acute cervical spinal cord injury

Precise evaluation of spinal cord compression and intramedullary lesions is essential for surgical decision-making in spinal cord injury (SCI). However, this process remains intrinsically subjective, frequently challenged by small lesion volumes, elongated morphology, and ambiguous boundaries, which collectively drive high inter-rater variability. We leveraged a heterogeneous multi-center MRI dataset (n = 711) to develop and validate an uncertainty-aware deep learning architecture.

The core multi-modal network captures long-range spatial dependencies, while a Monte Carlo dropout-based refinement module quantifies diagnostic uncertainty to resolve the inherent ambiguity of lesion boundaries. The framework achieved an overall Dice score of 70.44% for lesion segmentation. The integration of uncertainty refinement significantly improved algorithmic alignment with a 7-rater expert consensus (mean Dice: 65.85 vs. 63.59%).

The pipeline directly translates raw predictions into an anatomically anchored 3D visualization tool, localizing the maximum compressed level (MCL) with a mean absolute error of 5.72 mm and correctly mapping longitudinal lesion extent to exact vertebral levels in 80.8% of cases. Furthermore, imaging biomarkers exhibited significant inverse correlations with neurological impairment as measured by the American Spinal Injury Association Impairment Scale (AIS) grade and Upper Extremity Motor Score (UEMS) at admission, discharge and 3-month follow-up. This framework advances SCI evaluation from subjective visual assessment to standardized, quantitative outcome monitoring.

This work was supported by the National Key Research and Development Program of China (Grant No. 2025YFE0214100), the Department of Science and Technology of Jilin Province (Grant No. YDZJ202601ZYTS402), and the Jilin Province Medical and Health Talent Special Project (Grant No. 2025WSZX-JC07). The funders had no role in the study design, data collection, data analysis, interpretation of the data, preparation of the manuscript, or decision to submit the manuscript for publication.

These authors contributed equally: Qingzhi Xiang, Longhao Yang, Dacheng Sang, Yang Wang. Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China Qingzhi Xiang, Longhao Yang, Shaobo Cheng, Xiao Xia, Fangzheng Xu & Yan Yu Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Shanghai, China Department of Orthopaedics, Peking University Third Hospital, Beijing, China Dacheng Sang, Hongyu Chen, Xueshi Tian & Feifei Zhou Engineering Research Center of Bone and Joint Precision Medicine, Peking University, Beijing, China Dacheng Sang, Zhenxu Li, Hongyu Chen, Xueshi Tian & Feifei Zhou Beijing Key Laboratory of Advanced Bioadaptable Orthopedic Implants, Peking University, Beijing, China The Second Hospital of Jilin University, Changchun, China Spinextech Medical Technology Co., Ltd, Shanghai, China Department of Orthopedics, Beijing Anzhen Hospital, Capital Medical University, Beijing, China Correspondence to Feifei Zhou, Minfei Wu or Yan Yu. The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.