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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}
}
```
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