| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| language: |
| - en |
| size_categories: |
| - n<1K |
| tags: |
| - medical-imaging |
| - spine |
| - ct |
| - segmentation |
| - vertebra |
| - lstv |
| - tltv |
| - verse |
| pretty_name: "VerSeFusion: Re-fused VerSe 2019+2020 with VERIDAH corrections" |
| --- |
| |
| # VerSeFusion-Sample |
|
|
| A re-fused, PIR-canonical version of the VerSe 2019 and VerSe 2020 vertebra |
| segmentation challenges, with VERIDAH (Möller 2026) label corrections applied |
| for thoracolumbar transitional vertebrae. |
|
|
| ## Dataset stats |
|
|
| - **Total scans:** 10 |
| - **Total patients:** 10 |
| - **Splits:** training=1, validation=4, test=5 |
| - **Source:** VerSe 2019 + VerSe 2020 (combined) with VERIDAH corrections |
| - **Canonical orientation:** PIR (axis 0 = P, axis 1 = I, axis 2 = R) |
| - **VERIDAH-corrected subjects:** 0 |
|
|
| ## Orientation |
|
|
| Every scan in this dataset has been reoriented to a single canonical frame: |
|
|
| - **axis 0** increases toward **P** (posterior — i.e., anterior → posterior) |
| - **axis 1** increases toward **I** (inferior — i.e., superior → inferior; this is the spine axis) |
| - **axis 2** increases toward **R** (right — i.e., left → right) |
|
|
| This is verified end-to-end: see `orientation_audit.json` for the |
| per-subject report. Rendering conventions in `previews/`: |
|
|
| - **Coronal:** head at top, patient's right at viewer's right |
| - **Axial:** anterior at top, patient's right at viewer's right |
| - **Sagittal:** head at top, anterior at left |
|
|
| ## Structure |
|
|
| ``` |
| gregoryschwingmdphd/VerseFusion-Sample/ |
| ├── README.md |
| ├── LICENSE |
| ├── splits.csv # series_id → split (training/validation/test) |
| ├── orientation_audit.json # per-subject orientation verification |
| ├── scans/ |
| │ └── <series_id>/ |
| │ ├── ct.nii.gz # CT volume, HU values, PIR-oriented |
| │ ├── mask.nii.gz # vertebra labels (uint8), PIR-oriented |
| │ └── meta.json # per-scan provenance |
| ├── corrections/ |
| │ └── veridah_manifest.json # which subjects had labels corrected |
| └── previews/ # optional QC renders |
| └── <series_id>.png |
| ``` |
|
|
| ## Label schema |
|
|
| | Label | Anatomy | | Label | Anatomy | |
| |-------|---------|-|-------|---------| |
| | 1–7 | C1–C7 | | 20 | L1 | |
| | 8 | T1 | | 21 | L2 | |
| | 9 | T2 | | 22 | L3 | |
| | 10 | T3 | | 23 | L4 | |
| | 11 | T4 | | 24 | L5 | |
| | 12 | T5 | | 25 | L6 (supernumerary lumbar) | |
| | 13 | T6 | | 26 | sacrum (variably annotated) | |
| | 14 | T7 | | 27 | coccyx | |
| | 15 | T8 | | 28 | T13 (supernumerary thoracic) | |
| | 16 | T9 | | | | |
| | 17 | T10 | | | | |
| | 18 | T11 | | | | |
| | 19 | T12 | | | | |
|
|
| ## Loading example |
|
|
| ```python |
| import nibabel as nib |
| |
| ct = nib.load("scans/verse001/ct.nii.gz") |
| msk = nib.load("scans/verse001/mask.nii.gz") |
| |
| # Both are guaranteed to be PIR-oriented: |
| assert nib.aff2axcodes(ct.affine) == ('P', 'I', 'R') |
| assert nib.aff2axcodes(msk.affine) == ('P', 'I', 'R') |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original VerSe challenges and the |
| VERIDAH corrections paper: |
|
|
| ```bibtex |
| @article{sekuboyina2021verse, |
| title={VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images}, |
| author={Sekuboyina, A. and others}, |
| journal={Medical Image Analysis}, |
| year={2021} |
| } |
| |
| @article{loffler2020verse2020, |
| title={A vertebral segmentation dataset with fracture grading}, |
| author={Löffler, M.T. and others}, |
| journal={Radiology: Artificial Intelligence}, |
| year={2020} |
| } |
| |
| @article{moller2026veridah, |
| title={VERIDAH: Vertebral identification and transitional anomaly detection}, |
| author={Möller, H. and others}, |
| year={2026} |
| } |
| ``` |
|
|
| ## Acknowledgments |
|
|
| VerSe challenge data: Technical University Munich. VERIDAH corrections: |
| H. Möller et al. (2026). |
|
|
|
|
| ## Note: this is a sample |
|
|
| This is a 10-scan sample from the full dataset, chosen as the most-completely-labeled scans (highest unique-vertebra-label count, with VERIDAH-corrected subjects prioritized to showcase the thoracolumbar transitional-vertebra corrections). |
|
|
| For the full VerSeFusion dataset, see: https://huggingface.co/datasets/gregoryschwingmdphd/VerseFusion |
|
|
