This dataset supports the publication Comparing Linear and Nonlinear Finite Element Models of Vertebral Strength Across the Thoracolumbar Spine: A Benchmark from Density-Calibrated Computed Tomography (Walle, Matheson, Boyd; 2025, https://doi.org/10.1101/2025.04.19.649449). This dataset provides calibrated, resampled, and re-aligned CT images and segmentation masks for all non-fractured vertebrae from the VerSe'19 dataset, prepared for benchmarking finite element (FE) modeling of the thoracolumbar spine. The original VerSe 2019 dataset includes 160 CT scans of 141 patients. In this dataset, each vertebra is provided as a cropped NIfTI CT image and corresponding segmentation mask that separates cortical and trabecular compartments, vertebral body and processes, as well as anterior and posterior intervertebral disks for applying boundary conditions in FE analysis. Vertebrae have been individually reoriented such that the superior and inferior endplates of the vertebral body are approximately aligned with the axial plane, facilitating standardized model setup and loading conditions. Folder structure: nii_files/└── Subject/ ├── im/ │ ├── vertebra_20_im.nii.gz │ └── vertebra_21_im.nii.gz └── seg/ ├── vertebra_20_seg.nii.gz └── vertebra_21_seg.nii.gz calibration_files/└── Subject/ └── subject.txt The number in each filename (e.g., vertebra_20_im.nii.gz) corresponds to the original VerSe’19 vertebral label: 1–7 for C1–C7, 8–19 for T1–T12, and 20–25 for L1–L6. Labels 26 and 27 (sacrum and coccyx) as well as 28 (a 13th thoracic vertebra, T13). Segmentation labels: Trabecular bone of the vertebral body Cortical bone of the vertebral body Trabecular bone of the spinous process Cortical bone of the spinous process Posterior intervertebral disk Anterior intervertebral disk All segmentations were generated using 3D nnU-Net (v2) models trained on publicly available datasets. Vertebral substructure segmentation was based on VerSe’19 (https://doi.org/10.1148/ryai.2020190138), and reference tissue segmentation models were trained using TotalSegmentator (https://doi.org/10.5281/zenodo.10047292). The corresponding model weights are shared separately: vertebral substructures (https://doi.org/10.5281/zenodo.15238176) and reference tissues (https://doi.org/10.5281/zenodo.15238423). Calibration logs are provided to enable phantomless internal calibration of the original VerSe’19 scans (available at https://osf.io/nqjyw/) using the Ogo toolkit (https://github.com/Bonelab/Ogo), supporting recalibration for additional tissue compartments such as muscle or adipose tissue. While finite element outputs are not included in this deposit, they are tabulated and visualized in the associated GitHub repository (https://github.com/Bonelab/spineFE-benchmark), which also provides examples to generate all required models. Original data attribution: VerSe’19 dataset: https://osf.io/nqjyw/ Sekuboyina A et al. VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, Med Image Anal. 2021. https://doi.org/10.1016/j.media.2021.102166 Löffler M et al. A Vertebral Segmentation Dataset with Fracture Grading, Radiology: Artificial Intelligence. 2020. https://doi.org/10.1148/ryai.2020190138 Liebl H, Schinz D et al. A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data. Preprint: https://arxiv.org/pdf/2103.06360.pdf