| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| modality: |
| - CBCT |
| language: [] |
| tags: |
| - medical-imaging |
| - dental |
| - tooth-segmentation |
| - cbct |
| - 3d-segmentation |
| - semi-supervised |
| pretty_name: STS-3D-Tooth |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: integrity_labeled |
| path: data/integrity_labeled-* |
| - split: integrity_unlabeled |
| path: data/integrity_unlabeled-* |
| - split: roi_labeled |
| path: data/roi_labeled-* |
| - split: roi_unlabeled |
| path: data/roi_unlabeled-* |
| --- |
| |
| # STS-3D-Tooth |
|
|
| The 3D Cone-Beam CT (CBCT) subset of the STS (Semi-supervised Teeth |
| Segmentation) multi-modal dental dataset, as released in |
| [Wang et al., *Scientific Data* **12**, 117 (2025)](https://doi.org/10.1038/s41597-024-04306-9) |
| and used in the MICCAI 2023/2024 STS Challenges. |
|
|
| The companion 2D panoramic X-ray subset is hosted at |
| [`Angelou0516/STS-2D-Tooth`](https://huggingface.co/datasets/Angelou0516/STS-2D-Tooth). |
|
|
| ## Dataset Summary |
|
|
| | Field | Details | |
| |---|---| |
| | Modality | Cone-Beam CT (CBCT), NIfTI (`.nii.gz`) | |
| | Body Part | Teeth (32 permanent teeth, FDI numbering) | |
| | Volumes | 371 total: 32 labeled, 339 unlabeled | |
| | Volume shape | 512 x 512 x 400 (consistent across all volumes) | |
| | License | CC-BY-4.0 | |
| | Source | https://zenodo.org/records/10597292 | |
|
|
| ## Subsets |
|
|
| The release ships two distinct CBCT subsets that differ in field-of-view, intensity |
| representation, and label semantics. They are **not interchangeable**. |
|
|
| ### Integrity (whole-FOV scan) |
|
|
| Full head/jaw CBCT acquisitions captured in their original field of view. |
|
|
| - 10 labeled volumes (image + binary tooth-vs-background mask) |
| - 231 unlabeled volumes (image only, no GT) |
| - Image dtype: `float32`, intensity range `[0.0, 1.0]` (pre-normalized to unit interval) |
| - Affine: identity (`1.0 x 1.0 x 1.0` voxel spacing) — true acquisition spacing is **not** preserved |
| - Mask labels: `{0, 1}` — binary foreground = teeth |
|
|
| ### ROI (tooth-region crop) |
|
|
| Cropped sub-volumes centered on the dental arch. |
|
|
| - 22 labeled volumes (image + multi-class instance mask) |
| - 108 unlabeled volumes (image only, no GT) |
| - Image dtype: `int16`, intensity range approximately `[-1000, 3095]` (raw HU-like) |
| - Affine: real isotropic spacing, approximately `0.156 x 0.156 x 0.150` mm |
| - Mask labels: integer class indices > 0 — per-tooth instance labels following the FDI |
| numbering convention (not all 32 teeth appear in every volume) |
|
|
| | Subset | Total | Labeled | Unlabeled | |
| |-----------|------:|--------:|----------:| |
| | Integrity | 241 | 10 | 231 | |
| | ROI | 130 | 22 | 108 | |
| | **Total** | 371 | 32 | 339 | |
|
|
| ## Recommended Ground Truth |
|
|
| Annotation pipeline (per the source paper): |
| 1. 10 junior dentists each annotated CBCT scans layer-by-layer in ITK-SNAP. |
| 2. 3 senior dentists (>10 years experience) reviewed and corrected the layer-wise |
| annotations. |
| 3. Remaining inter-reviewer discrepancies were resolved by consensus. |
|
|
| The shipped masks are post-consensus refined and reflect senior-expert agreement. |
| There is no alternative mask source. |
|
|
| ## Data Structure |
|
|
| ``` |
| STS-3D-Tooth/ |
| |-- README.md |
| |-- Integrity/ |
| | |-- Labeled/ |
| | | |-- Image/Integrity_L_NNN.nii.gz # 10 CBCT volumes (float32, [0,1]) |
| | | `-- Mask/Integrity_L_NNN.nii.gz # 10 binary masks |
| | `-- Unlabeled/ |
| | `-- Image/Integrity_U_NNN.nii.gz # 231 unlabeled CBCT volumes |
| `-- ROI/ |
| |-- Labeled/ |
| | |-- Image/ROI_L_NNN.nii.gz # 22 CBCT crops (int16, raw HU-like) |
| | `-- Mask/ROI_L_NNN.nii.gz # 22 multi-class FDI instance masks |
| `-- Unlabeled/ |
| `-- Image/ROI_U_NNN.nii.gz # 108 unlabeled CBCT crops |
| ``` |
|
|
| Image and mask filenames match exactly within each `Labeled/` directory. |
|
|
| ## Splits |
|
|
| The released dataset has **no official train/val/test split** — define your own |
| downstream. The labeled / unlabeled distinction is intrinsic to the |
| semi-supervised challenge format; the unlabeled volumes have no ground truth and |
| are typically used only for self-training or pretext objectives. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2025sts, |
| title = {A multi-modal dental dataset for semi-supervised deep learning image segmentation}, |
| author = {Wang, Yaqi and others}, |
| journal = {Scientific Data}, |
| volume = {12}, |
| pages = {117}, |
| year = {2025}, |
| doi = {10.1038/s41597-024-04306-9} |
| } |
| |
| @article{wang2024stschallenge, |
| title = {STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation}, |
| author = {Wang, Yaqi and others}, |
| journal = {arXiv:2407.13246}, |
| year = {2024} |
| } |
| ``` |
|
|
| ## License |
|
|
| CC-BY-4.0 (per the Zenodo release at |
| [zenodo.org/records/10597292](https://zenodo.org/records/10597292)). |
|
|
