---
license: cc-by-nc-sa-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - ct
  - abdomen
  - multi-organ
  - segmentation
  - flare21
  - 3d
size_categories:
  - n<1K
---

# FLARE21 — MICCAI 2021 FLARE Abdominal Organ Segmentation (re-host)

Re-host of the **FLARE21** labeled training set (361 cases) from the
[Zenodo record 5903672](https://zenodo.org/records/5903672), restructured into the
`dataset/case_XXXXX/` + `train.jsonl` layout shared with KiTS19/KiTS23/SLIVER07 so a single
`Base3DDataset` subclass can load it.

## Composition

| Split | Cases | With mask |
|-------|------:|----------:|
| train | 361   | yes       |

The FLARE21 validation/test sets (hidden, server-side scoring) are not included.

## Mask labels

| Value | Class    |
|-------|----------|
| 0     | background |
| 1     | liver    |
| 2     | kidney   |
| 3     | spleen   |
| 4     | pancreas |

## Files

```
dataset/case_00000/imaging.nii.gz        # abdominal CT
dataset/case_00000/segmentation.nii.gz   # 0..4 label volume
...
train.jsonl
```

`patient_id` is the new contiguous `case_XXXXX`; `source_id` keeps the upstream `train_NNN` id.

## License

Per the FLARE21 challenge terms, treat as **CC BY-NC-SA 4.0** (non-commercial). (The Zenodo
record tags CC-BY-4.0; the challenge data page states CC-BY-NC-SA — the more restrictive
non-commercial terms are used here.) Cite:

```bibtex
@article{ma2022flare,
  title   = {Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge},
  author  = {Ma, Jun and Zhang, Yao and Gu, Song and others},
  journal = {Medical Image Analysis},
  volume  = {82},
  pages   = {102616},
  year    = {2022}
}
```