| --- |
| 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} |
| } |
| ``` |
|
|
