# Brats Peds 2023 Eval

> Volumetric segmentation of pediatric brain gliomas using multi-institutional MRI data. It probes a model's ability to accurately delineate tumor sub-regions (enhancing tumor, peritumoral edema, necrotic/cystic core) in 3D MRI scans. Use when the user wants to benchmark on BraTS-PEDs 2023, or asks about evaluating this task. Reports Dice Score.

- Skill: `qhjqhj00/brats-peds-2023-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/brats-peds-2023-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/brats-peds-2023-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-peds-2023-eval

---


# brats-peds-2023-eval

> BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023 — Fathi Kazerooni et al. (2024) (arXiv:2407.08855, 2024)

## What this evaluates

Volumetric segmentation of pediatric brain gliomas using multi-institutional MRI data. It probes a model's ability to accurately delineate tumor sub-regions (enhancing tumor, peritumoral edema, necrotic/cystic core) in 3D MRI scans.

## Datasets

- **BraTS-PEDs 2023** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `Dice Score` **(primary)** — range: [0, 1]
  - 2 * |A ∩ B| / (|A| + |B|), measuring voxel-wise overlap between prediction and ground truth for each tumor sub-region.
- `Hausdorff Distance 95% (HD95)` — range: mm
  - 95th percentile of the maximum surface distance between prediction and ground truth boundaries, measuring spatial boundary accuracy.

## Input / output format

**Input**: Multi-sequence 3D volumetric MRI scans (T1, T1ce, T2, FLAIR) with corresponding tumor segmentation masks.

**Output**: 3D segmentation mask predicting tumor sub-regions (enhancing tumor, peritumoral edema, necrotic/cystic core).

## Scoring recipe

```python
import numpy as np
def dice_score(pred, gt):
    intersection = np.sum(pred * gt)
    return 2.0 * intersection / (np.sum(pred) + np.sum(gt))
def hd95(pred, gt):
    from scipy.spatial.distance import cdist
    surfaces_pred = np.array(np.where(pred > 0)).T
    surfaces_gt = np.array(np.where(gt > 0)).T
    if len(surfaces_pred) == 0 or len(surfaces_gt) == 0:
        return float('inf')
    dists = cdist(surfaces_pred, surfaces_gt)
    return np.percentile(dists, 95)
```

## Common pitfalls

- Confusing pediatric BraTS-PEDs with adult BraTS datasets, which have different tumor distributions and scanner protocols.
- Failing to preprocess MRI sequences (e.g., N4 bias field correction, normalization) consistently across institutions, leading to domain shift.
- Evaluating on 2D slices instead of full 3D volumes, which violates the challenge's volumetric metric requirements.

## Evidence (verbatim from paper)

> The challenge establishes standardized evaluation metrics including the Dice Score across diverse clinical datasets, enabling reproducible, scalable volumetric analysis critical for clinical trial response assessment in pediatric neuro-oncology.

## Citation

```bibtex
@misc{fathikazerooni2024bratspeds,
  title={BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023},
  author={Fathi Kazerooni et al. (2024)},
  year={2024},
  note={arXiv:2407.08855}
}
```

- arXiv: 2407.08855