Manual single-slice lumbar quantitative computed tomography (QCT) depends on operator-driven slice selection and trabecular region-of-interest (ROI) placement. We developed a fully automated single-slice workflow for vertebral trabecular Hounsfield unit (HU) measurement that combines unsuitable-slice prescreening, dual-purpose segmentation, intra-patient slice-quality ranking, and a deterministic inner ROI rule. The pipeline includes an Eligibility Gate, QC-Envelope segmentation for broad, vertebral- and usability-preserving delineation, PairRank-Swin for best-slice selection, and dedicated trabecular segmentation for final quantitative analysis. In the independent external cohort, 4 cases were considered non-evaluable by both manual review and the pipeline, and 2 additional borderline-quality cases were manually measured but rejected by the pipeline; therefore, paired HU agreement analysis included 44 evaluable cases. Agreement remained high, with Pearson’s r = 0.987, Lin’s CCC = 0.985, mean bias −0.44 HU, and limits of agreement from −14.88 to +13.99 HU. Coverage was 84.1% within ±10 HU and 97.7% within ±15 HU. Ablation analysis showed that slice ranking and ROI erosion were the most critical components. In an open module-level baseline comparison, QC-Envelope segmentation substantially outperformed TotalSegmentator. This workflow provides high agreement with expert HU measurement while preserving reviewable intermediate outputs.

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.

Automated lumbar computed tomography (CT) Hounsfield unit (HU) measurement can support opportunistic osteoporosis screening, but heterogeneous Digital Imaging and Communications in Medicine (DICOM) inputs often disrupt automated workflows before measurement. We evaluated a local Qwen2.5-VL-7B vision-language model (VLM)-assisted front end for suitability routing, input planning, and HU-traceable DICOM-derived field-of-view/orientation adaptation upstream of an unchanged single-slice lumbar CT HU workflow. The VLM was restricted to routing and planning. In a 20-case FUJIFILM pilot cohort, observed HU output availability was higher with the front-end-assisted route than with direct processing (12/20 versus 5/20), while paired automatic-success cases showed excellent agreement with post hoc manual ImageJ (version 1.54g) measurements (ICC(A,1) = 0.997; MAE = 4.64 HU). Public DICOM stress testing further demonstrated improved workflow robustness across heterogeneous datasets. These findings support the use of a deployment-oriented front-end strategy to improve the auditability and robustness of automated lumbar CT HU measurement while preserving an unchanged downstream measurement workflow.