| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - image-segmentation |
| language: |
| - en |
| tags: |
| - medical |
| - image |
| - ct |
| - spine |
| - vertebrae |
| - segmentation |
| - landmarks |
| pretty_name: 'verse-lite' |
| size_categories: |
| - n<1K |
| --- |
| |
|
|
| ## About |
| This is a preprocessed redistribution of [VerSe'19 + VerSe'20](https://github.com/anjany/verse), which is released under the `CC BY-SA 4.0` license. |
|
|
| **Dataset summary:** 325 spine CT scans with per-vertebra segmentation masks, and lumbar (L1-L5) centroid landmarks for 250 of them. |
|
|
| **Contents of this repository:** |
|
|
| - `Images/` — 325 files |
| - `Masks/` — 325 files |
|
|
| 📝 Landmark annotations, visualization figures and the benchmark plan files live in 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can load the complete images and annotations from dataset configs. |
|
|
|
|
| ## Relation to the source dataset |
|
|
| | | | |
| | --- | --- | |
| | In the source | 374 CT series across VerSe'19 + VerSe'20 | |
| | Excluded here | 30 `sub-gl*` scans (CC BY-NC-ND) and 19 duplicate `_split-verse<NNN>` series | |
| | **In this repo** | **325 `Images` + 325 `Masks`** | |
|
|
| The 30 `sub-gl*` scans are **excluded**: that imaging is released under `CC BY-NC-ND`, which forbids derivative works, so no derived annotation can be redistributed for them. A further 19 series are dropped by de-leaking — ~18 subjects were scanned as 2-3 separate `_split-verse<NNN>` series, and keeping them all would place the same spine in both the train and test split. 374 scans upstream -> 344 redistributable -> **325 shipped**. |
|
|
| **Why `-Lite`?** The suffix marks this as a *derived* redistribution rather than a copy of the source. These are **preprocessed** volumes — every case has been format-converted, geometry-normalised and reoriented to RAS+ — and for some sources cases or modalities are excluded as well (see the table above). Use it to reproduce MedVision, not as a substitute for the original release. See [Preprocessing](#preprocessing) below for exactly what was changed. |
|
|
|
|
| ## Preprocessing |
|
|
| - Images and masks standardized to RAS+ orientation; masks cast to `uint16` on the image grid. |
|
|
| - Geometry is read via nibabel's `.affine`, which resolves from QFORM (VerSe files carry `sform_code=0, qform_code=1`, so reading SFORM directly would give a zero matrix). |
|
|
| - Vertebral centroids from the challenge `*_ctd.json` files are **voxel indices in the native orientation** (not world-mm); they are mapped through the native->world->RAS+ chain to 0-based indices in the shipped volume. |
|
|
| - macOS resource forks (`__MACOSX`, `._*`) in the source archives are filtered out. |
|
|
| 📝 Field of view varies from cervical-only to whole-body, so only 250 of the 325 scans contain all of L1-L5. Those form the `Images-lumbar/` subset used by the biometry task. |
|
|
|
|
| ## Segmentation Labels |
|
|
| ```python |
| labels_map = { |
| "1": "vertebra C1", |
| "2": "vertebra C2", |
| "3": "vertebra C3", |
| "4": "vertebra C4", |
| "5": "vertebra C5", |
| "6": "vertebra C6", |
| "7": "vertebra C7", |
| "8": "vertebra T1", |
| "9": "vertebra T2", |
| "10": "vertebra T3", |
| "11": "vertebra T4", |
| "12": "vertebra T5", |
| "13": "vertebra T6", |
| "14": "vertebra T7", |
| "15": "vertebra T8", |
| "16": "vertebra T9", |
| "17": "vertebra T10", |
| "18": "vertebra T11", |
| "19": "vertebra T12", |
| "20": "vertebra L1", |
| "21": "vertebra L2", |
| "22": "vertebra L3", |
| "23": "vertebra L4", |
| "24": "vertebra L5", |
| "25": "vertebra L6", |
| "28": "vertebra T13" |
| } |
| ``` |
|
|
|
|
| ## Landmarks |
|
|
| ```python |
| landmarks_map = { |
| "P1": "centroid of vertebra L1", |
| "P2": "centroid of vertebra L2", |
| "P3": "centroid of vertebra L3", |
| "P4": "centroid of vertebra L4", |
| "P5": "centroid of vertebra L5" |
| } |
| ``` |
|
|
|
|
| ## News |
| - [25 Jul, 2026] Initial release. This dataset is integrated into 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can use these config names to load data in python: |
|
|
| - `VerSe_BiometricsFromLandmarks_Task01_Sagittal_Test` |
| - `VerSe_BiometricsFromLandmarks_Task01_Sagittal_Train` |
| - `VerSe_BoxSize_Task01_Axial_Test` |
| - `VerSe_BoxSize_Task01_Axial_Train` |
| - `VerSe_BoxSize_Task01_Coronal_Test` |
| - `VerSe_BoxSize_Task01_Coronal_Train` |
| - `VerSe_BoxSize_Task01_Sagittal_Test` |
| - `VerSe_BoxSize_Task01_Sagittal_Train` |
| - `VerSe_MaskSize_Task01_Axial_Test` |
| - `VerSe_MaskSize_Task01_Axial_Train` |
| - `VerSe_MaskSize_Task01_Coronal_Test` |
| - `VerSe_MaskSize_Task01_Coronal_Train` |
| - `VerSe_MaskSize_Task01_Sagittal_Test` |
| - `VerSe_MaskSize_Task01_Sagittal_Train` |
|
|
|
|
| ## Data Usage Agreement |
| By using the dataset, you agree to the terms as follow. |
| - You must comply with the original `CC BY-SA 4.0` license terms of the source dataset. |
| - You are recommended to refer to the source of this dataset in any publication: `https://huggingface.co/datasets/YongchengYAO/VerSe-Lite` |
| - You must cite the original publication(s): |
| - https://doi.org/10.1016/j.media.2021.102166 |
|
|
|
|
| ## Official Release |
| For more information, please go to the official site: https://github.com/anjany/verse |
|
|
|
|
| ## Download from Huggingface |
| ```python |
| # python |
| from huggingface_hub import snapshot_download |
| snapshot_download(repo_id="YongchengYAO/VerSe-Lite", repo_type='dataset', local_dir="/your/local/folder") |
| ``` |
|
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|
