Datasets:
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license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- ct
- chest
- lung
- airway
- segmentation
- atm22
- 3d
size_categories:
- n<1K
---
# ATM22 — Airway Tree Modeling Challenge 2022 (re-mirror)
Re-hosted mirror of the **ATM22** challenge training set (Zhang et al.,
*Medical Image Analysis* 2023, arXiv:2303.05745), originally distributed
through the [ATM22 Grand Challenge](https://atm22.grand-challenge.org/).
This mirror rebuilds the data from the authors' own
[Zenodo re-publication](https://zenodo.org/records/6590745) under
CC BY 4.0, restructured into the same layout we use for KiTS23 / KiPA22 /
AbdomenCT1K / etc. so a single `Base3DDataset` subclass can load it.
## Composition
| Split | Cases | With mask |
|-------|------:|----------:|
| train | 299 | yes |
Original challenge composition: 300 train + 50 val (image-only) + 150 test
(withheld). This mirror ships **train only** (300 cases minus 1 for
`ATM_164` whose label is misaligned with its corresponding image per
challenge errata = 299 usable cases). The val and test sets have no
public masks and so cannot be evaluated locally; refetch them from
Zenodo `imagesVal.rar` if you want inference-only inputs.
## File layout
```
dataset/ATM_001/
imaging.nii.gz
segmentation.nii.gz
...
dataset/ATM_500/
train.jsonl
README.md
```
`train.jsonl` lists one entry per case with `image`, `mask`, `label`,
`modality`, `dataset`, `official_split`, `patient_id` keys. Image/mask
paths are prefixed with `data/nii/ATM22/` so they slot directly into
the EasyMedSeg `Base3DDataset` `HF_JSONL_PREFIX` convention.
## Mask labels
Binary airway mask:
| Value | Class |
|-------|------------|
| 0 | background |
| 1 | airway |
The airway annotation includes trachea, main bronchi, lobar bronchi, and
segmental bronchi as one composite class (no branch-level subclasses in
the public release).
## License
CC BY 4.0, inherited from the upstream Zenodo mirror. The official
challenge organizers' research-only terms apply on top. Cite the
benchmark paper:
```bibtex
@article{zhang2023multi,
title = {Multi-site, multi-domain airway tree modeling (ATM'22):
A public benchmark for pulmonary airway segmentation},
author = {Zhang, Minghui and Wu, Yangqian and Zhang, Hanxiao and others},
journal = {Medical Image Analysis},
year = {2023},
doi = {10.1016/j.media.2023.102957}
}
```
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