# RSNA 2025 Aneurysm — 26-class Vessel-Anatomy and Aneurysm Segmentation Labels

26-class vessel-anatomy and aneurysm segmentation labels (13 vessel anatomy
classes + 13 aneurysm location classes, values 0–26, see `labels.json`) for
the RSNA 2025 Intracranial Aneurysm Detection challenge, placed back into the
original image space (pure voxel placement, no resampling).

**Paper:** [arXiv:2606.26706](https://arxiv.org/abs/2606.26706)

## Contents

| Folder | Count | Aligned to |
|---|---|---|
| `labelsTr_26classes_in_orig_space/` | 4317 | `imagesTr/<uid>_0000.nii.gz` (see `REPRODUCE.md`) |
| `labelsTr_multiframe_cor_sag/` | 27 | coronal/sagittal renders of multiframe MR DICOMs (see below) |

## Format

- NIfTI, uint8, affine identical to the paired image (spacing / origin / direction).
- Placement: pure voxel placement, **no resampling**; voxels outside the ROI are 0.
- File names are `SeriesInstanceUID.nii.gz`, matching the image name of the same UID.

## Reproducing the images

Per RSNA rules, the original DICOMs must be downloaded from Kaggle:
[https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection/data](https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection/data)

Processing code (DICOM → NIfTI):
[https://github.com/PengchengShi1220/RSNA2025_Intracranial-Aneurysm-Detection/blob/master/process_RSNA2025_all_data.py](https://github.com/PengchengShi1220/RSNA2025_Intracranial-Aneurysm-Detection/blob/master/process_RSNA2025_all_data.py)

See `REPRODUCE.md` for the exact pipeline.

## The 27 multiframe cases

These are multiframe MR series whose DICOM headers required special handling
(direction/spacing) during conversion. Their labels live in the coronal/sagittal
space produced by dcm2niix, which differs from the axial output of
`process_RSNA2025_all_data.py`. **Do not** overlay them on `imagesTr/<uid>`
directly — reproduce their paired images via dcm2niix on the same multiframe DICOMs.

## License

- **Labels**: CC-BY-NC 4.0 (non-commercial use only, aligned with the RSNA 2025
  competition winner license). These are derived annotations; the original
  images are **not** redistributed — download them from the official RSNA/AWS
  source (see above).

Please cite our work if it is helpful for your research:

```bibtex
@misc{shi2026intracranialaneurysmclassificationsegmentation,
      title={Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning},
      author={Pengcheng Shi and Kaiyuan Yang and Houjing Huang and Jiawei Chen and Yan Lu and Jiaqi Liu and Murong Xu and Minghui Zhang and Bjoern Menze and Xinglin Zhang},
      year={2026},
      eprint={2606.26706},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.26706},
}
```