---
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)).