A Vertebra-Aware Framework for Structured Analysis of Post-Fracture Lumbar CT

Routine computed tomography (CT) provides an opportunity for opportunistic vertebra-aware analysis beyond its original acquisition purpose. In this work, we study the engineering feasibility of transforming routine post-fracture lumbar CT into a compact structured case summary, rather than producing only a single black-box prediction. We propose a vertebra-aware 3D multi-task learning framework that jointly performs vertebral segmentation, density-related descriptor estimation, CT- and geometry-derived structure-aware descriptor estimation, vertebra-level fracture-related auxiliary modeling, derived case-level summary generation, and quality-control/uncertainty-aware output organization. The structure-aware descriptor is introduced as a framework-defined quantitative field for organizing density-related signal distribution and vertebral geometry on the current scan, not as a validated biomechanical measurement or intrinsic-strength estimator. Experiments on xVertSeg using five-fold case-level cross-validation show that the framework can generate coherent vertebra-wise structured outputs and support preliminary derived case-level discriminative analysis under limited supervision. To partially address the small-sample limitation, supplementary experiments on VerSe 2020 are conducted for external anatomical generalization and anatomical pretraining. The results indicate that VerSe-based pretraining improves segmentation stability and downstream descriptor consistency after xVertSeg fine-tuning. Overall, this study should be interpreted as an engineering proof-of-concept for report-oriented structured analysis of post-fracture lumbar CT, rather than as prospective prediction, biomechanical validation, or a clinically deployed decision-support system.