---
license: cc-by-4.0
task_categories:
- image-segmentation
modality:
- CT
language: []
tags:
- medical-imaging
- airway-segmentation
- lung-segmentation
- thoracic-CT
pretty_name: AeroPath
size_categories:
- n<100
dataset_info:
  features:
  - name: subject_id
    dtype: int32
  - name: num_slices
    dtype: int32
  - name: ct_middle_slice
    dtype: image
  - name: mask_middle_slice
    dtype: image
  - name: overlay_middle_slice
    dtype: image
  splits:
  - name: train
    num_bytes: 10537061
    num_examples: 27
  download_size: 10544507
  dataset_size: 10537061
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# AeroPath

**AeroPath** is an airway segmentation benchmark dataset with challenging pathology, containing 27 contrast-enhanced CT scans acquired at St. Olavs Hospital, Trondheim, Norway.

## Dataset Summary

| Field | Details |
|---|---|
| Modality | Contrast-enhanced CT (CTA) |
| Body Part | Chest — airways and lungs |
| Subjects | 27 |
| Labels | Airways, Lungs |
| Total Size | ~4.8 GB |
| License | CC-BY 4.0 |

## Data Structure

Each subject folder contains:
- `{N}_CT_HR.nii.gz` — CT volume
- `{N}_CT_HR_label_airways.nii.gz` — airway segmentation mask
- `{N}_CT_HR_label_lungs.nii.gz` — lung segmentation mask

## Citation

```bibtex
@dataset{hofstad2023aeropathzenodo,
  title     = {AeroPath: An airway segmentation benchmark dataset with challenging pathology},
  author    = {Hofstad, Erlend and Bouget, David and Pedersen, André},
  month     = nov,
  year      = 2023,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.10069289},
  url       = {https://doi.org/10.5281/zenodo.10069289}
}

@article{stoverud2024aeropath,
  title   = {AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method},
  author  = {Støverud, Karen-Helene and Bouget, David and Pedersen, André and Langø, Thomas and Hofstad, Erlend Fagertun and others},
  journal = {PLOS ONE},
  volume  = {19},
  number  = {10},
  pages   = {e0311416},
  year    = {2024},
  doi     = {10.1371/journal.pone.0311416}
}
```