# Brats Africa Eval

> Evaluates the accuracy of deep learning models in segmenting brain tumor subregions and boundaries on low-field MRI scans from Sub-Saharan Africa. It probes the model's ability to handle regional imaging protocol limitations and topological deformations in medical image segmentation. Use when the user wants to benchmark on BraTS-Africa, or asks about evaluating this task. Reports Dice Similarity Coefficient (DSC).

- Skill: `qhjqhj00/brats-africa-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/brats-africa-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/brats-africa-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/brats-africa-eval

---


# brats-africa-eval

> Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset — Bohara et al. (2026) (arXiv:2604.15964, 2026)

## What this evaluates

Evaluates the accuracy of deep learning models in segmenting brain tumor subregions and boundaries on low-field MRI scans from Sub-Saharan Africa. It probes the model's ability to handle regional imaging protocol limitations and topological deformations in medical image segmentation.

## Datasets

- **BraTS-Africa** — total ?; splits: test (-1)

## Metrics

- `Dice Similarity Coefficient (DSC)` **(primary)** — range: [0, 1]
  - DSC = 2|A ∩ B| / (|A| + |B|), where A and B are the predicted and ground truth segmentation masks. Ranges from 0 to 1, with 1 indicating perfect overlap.
- `Normalized Surface Distance (NSD)` — range: [0, 1]
  - NSD = 1 - (1/N) * Σ min(dist(a, B), tol) + min(dist(b, A), tol) / (2*tol), evaluated with a 1.0 mm tolerance. Ranges from 0 to 1, with 1 indicating perfect surface alignment.

## Input / output format

**Input**: 3D low-field MRI scans of brain tumors from the Sub-Saharan Africa dataset.

**Output**: Pixel-wise segmentation masks for three tumor subregions: SNFH, NETC, and ET, plus a Legacy region mask.

## Scoring recipe

```python
def compute_dsc(pred, gt):
    intersection = np.sum(pred & gt)
    return 2.0 * intersection / (np.sum(pred) + np.sum(gt))

def compute_nsd(pred, gt, tol=1.0):
    # Compute surface distances between pred and gt masks
    # NSD = 1 - (1/N) * sum(min(d_a, tol) + min(d_b, tol)) / (2*tol)
    # Returns value in [0, 1]
    pass
```

## Common pitfalls

- NSD is computed with a specific 1.0 mm tolerance, which must be explicitly set to match reported values.
- DSC is reported per subregion (SNFH, NETC, ET) and per anatomical grouping (Legacy, Lesion); averaging across regions without weighting can misrepresent performance.
- Pre-training on BraTS-2025 Task 1 is part of the evaluation protocol, not just training; models must be fine-tuned on BraTS-Africa before testing.

## Evidence (verbatim from paper)

> Our Baseline Model, nnU-Net 3D full resolution performed well overall on the BraTS-Africa dataset achieving high Dice scores for each tumor subregion: 0.930 for SNFH, 0.906 for NETC, and 0.906 for ET. The boundary alignment was demonstrated on NSD with a tolerance of 1.0 mm, for SNFH (0.830), NETC (0.827), and ET (0.894).

## Citation

```bibtex
@misc{bohara2026topology,
  title={Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset},
  author={Bohara et al. (2026)},
  year={2026},
  note={arXiv:2604.15964}
}
```

- arXiv: 2604.15964