# Brats2021 Seg Eval

> Evaluates 3D semantic segmentation of brain tumor sub-regions on multi-modal MRI scans. It probes the model's ability to capture long-range spatial dependencies and multi-scale contextual information for precise tumor boundary delineation. Use when the user wants to benchmark on BraTS 2021, or asks about evaluating this task. Reports Dice score.

- Skill: `qhjqhj00/brats2021-seg-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/brats2021-seg-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/brats2021-seg-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/brats2021-seg-eval

---


# brats2021-seg-eval

> Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images — Hatamizadeh et al. (2022) (arXiv:2201.01266, 2022)

## What this evaluates

Evaluates 3D semantic segmentation of brain tumor sub-regions on multi-modal MRI scans. It probes the model's ability to capture long-range spatial dependencies and multi-scale contextual information for precise tumor boundary delineation.

## Datasets

- **BraTS 2021** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `Dice score` **(primary)** — range: [0, 1]
  - Harmonic mean of precision and recall per voxel class, averaged across Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC).
- `Hausdorff distance` — range: other
  - 95th percentile Hausdorff distance in millimeters, measuring the maximum boundary discrepancy between predicted and ground truth segmentations.

## Input / output format

**Input**: Multi-modal 3D MRI volumes (FLAIR, T1, T1ce, T2) with corresponding voxel-wise segmentation masks for tumor sub-regions.

**Output**: 3D voxel-wise probability maps or binary masks for three tumor classes: Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC).

## Scoring recipe

```python
def dice_score(pred, gt):
    intersection = np.sum(pred * gt)
    union = np.sum(pred) + np.sum(gt)
    return 2.0 * intersection / (union + 1e-6)

def hausdorff_95(pred, gt):
    dists = distance_transform_edt(~pred) + distance_transform_edt(~gt)
    return np.percentile(dists, 95)

# Average across ET, WT, TC classes
mean_dice = np.mean([dice_score(p, g) for p, g in zip(preds, gts)])
mean_hd = np.mean([hausdorff_95(p, g) for p, g in zip(preds, gts)])
```

## Common pitfalls

- Dice score is class-averaged; reporting only one class (e.g., WT) overestimates overall performance.
- Hausdorff distance is highly sensitive to outliers; the paper reports mean values but does not explicitly state if 95% HD or full HD is used, though BraTS standard is 95% HD.
- Internal 5-fold CV uses a different split than the official BraTS 2021 test set, making direct comparison to challenge rankings tricky.

## Evidence (verbatim from paper)

> Evaluation results across all five folds are presented in Table 2. The proposed Swin UNETR model outperforms all competing approaches across all 5 folds and on average for all semantic classes (e.g. ET, WT, TC). Specifically, Swin UNETR outperforms the closest competing approaches by $0.7\%,0.6\%$ and $0.4\%$ for ET,WT and TC classes respectively and on average $0.5\%$ across all classes in all folds. ... Table 4: BraTS 2021 testing dataset benchmarks in terms of mean Dice score and Hausdorff distance values.

## Citation

```bibtex
@misc{hatamizadeh2022swinunetr,
  title={Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images},
  author={Hatamizadeh et al. (2022)},
  year={2022},
  note={arXiv:2201.01266}
}
```

- arXiv: 2201.01266