Brats20 Segmentation Eval

---
name: brats20-segmentation-eval
description: Evaluates the capability of 2D and 3D convolutional neural networks to segment brain tumor sub-regions (enhancing tumor, whole tumor, tumor core) from multi-modal volumetric MRI scans. It specifically probes the effectiveness of ImageNet pretraining and architectural extensions on segmentation accuracy and robustness across benchmark and private clinical data. Use when the user wants to benchmark on BraTS 2020, Syrian-Lebanese Hospital Clinical Dataset, or asks about evaluating this task. Reports Dice score.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1912.12452
  bibtex_key: [REDACTED]
  confidence: high
---

# brats20-segmentation-eval

> Transfer Learning for Brain Tumor Segmentation — Wacker et al. (2019) (arXiv:1912.12452, 2019)

## What this evaluates

Evaluates the capability of 2D and 3D convolutional neural networks to segment brain tumor sub-regions (enhancing tumor, whole tumor, tumor core) from multi-modal volumetric MRI scans. It specifically probes the effectiveness of ImageNet pretraining and architectural extensions on segmentation accuracy and robustness across benchmark and private clinical data.

## Datasets

- **BraTS 2020** — total ?; splits: train (-1), val (-1), test (-1)
- **Syrian-Lebanese Hospital Clinical Dataset** — total 5; splits: test (5)

## Metrics

- `Dice score` **(primary)** — range: [0, 1]
  - 2 * |prediction ∩ ground_truth| / (|prediction| + |ground_truth|). Computed per voxel for each tumor sub-region (ET, WT, TC) and averaged across 5 runs.
- `Hausdorff distance` — range: other
  - Maximum distance between the boundaries of the prediction and ground truth masks. Reported in millimeters.

## Input / output format

**Input**: Multi-modal volumetric MRI scans (T1c, T2, FLAIR) preprocessed to isotropic 1mm resolution, skull-stripped, and rigidly co-registered. During training, data is fed as patches of size (24x128x128) voxels with modalities treated as RGB channels.

**Output**: Volumetric segmentation masks predicting three tumor sub-regions: Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC).

## Scoring recipe

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

def hausdorff_distance(pred, gt):
    from scipy.spatial.distance import directed_hausdorff
    pred_boundary = np.argwhere(pred == 1)
    gt_boundary = np.argwhere(gt == 1)
    hd1 = directed_hausdorff(pred_boundary, gt_boundary)[0]
    hd2 = directed_hausdorff(gt_boundary, pred_boundary)[0]
    return max(hd1, hd2)
```

## Common pitfalls

- MRI contrasts and resolutions vary significantly across clinical patients, requiring careful reverse-engineering of the BraTS preprocessing pipeline (DICOM to NIFTI, skull stripping, co-registration, resampling).
- The private clinical dataset lacks the T1 modality used in BraTS, forcing the use of T1c for both T1 and T1c channels, which may affect performance.
- Outliers are frequent in clinical evaluation, suggesting that mean/median scores alone may mask robustness issues.

## Evidence (verbatim from paper)

> Table 1 compares mean/median dice scores and Hausdorff distances for all segmentation labels and methods taken over 5 runs for the validation data and a single test run of the best-performing method.

## Citation

```bibtex
@misc{wacker2019transfer,
  title={Transfer Learning for Brain Tumor Segmentation},
  author={Wacker et al. (2019)},
  year={2019},
  note={arXiv:1912.12452}
}
```

- arXiv: 1912.12452