Prior Loss Seg Benchmark

---
name: prior-loss-seg-benchmark
description: Evaluates the effectiveness of various prior-based loss functions (low-level boundary/distance and high-level shape/size constraints) for medical image segmentation across diverse anatomical structures and imaging modalities. Use when the user wants to benchmark on WMH, ISLES, Atrium, Colon, Spleen, Hippocampus, Prostate, ACDC, or asks about evaluating this task. Reports Dice score.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2201.02428
  bibtex_key: [REDACTED]
  confidence: high
---

# prior-loss-seg-benchmark

> Effect of Prior-based Losses on Segmentation Performance: A Benchmark — El Jurdi et al. (2022) (arXiv:2201.02428, 2022)

## What this evaluates

Evaluates the effectiveness of various prior-based loss functions (low-level boundary/distance and high-level shape/size constraints) for medical image segmentation across diverse anatomical structures and imaging modalities.

## Datasets

- **WMH** — total ?; splits: test (-1)
- **ISLES** — total ?; splits: test (-1)
- **Atrium** — total ?; splits: test (-1)
- **Colon** — total ?; splits: test (-1)
- **Spleen** — total ?; splits: test (-1)
- **Hippocampus** — total ?; splits: test (-1)
- **Prostate** — total ?; splits: test (-1)
- **ACDC** — total ?; splits: test (-1)

## Metrics

- `Dice score` **(primary)** — range: [0, 1]
  - 2 * |A ∩ B| / (|A| + |B|), where A and B are the predicted and ground truth segmentation masks respectively.
- `Hausdorff distance` — range: other
  - Maximum of the directed distances between the boundaries of the predicted and ground truth masks: max(sup_{a∈A} inf_{b∈B} d(a,b), sup_{b∈B} inf_{a∈A} d(a,b)).
- `MAE on connected components` — range: other
  - Average absolute difference between the number of connected components (instances) in the ground truth and the predicted segmentation map across all samples.

## Input / output format

**Input**: 3D medical image volumes with corresponding ground truth segmentation masks.

**Output**: Predicted segmentation map of the same spatial dimensions as the input image.

## Scoring recipe

```python
def compute_metrics(pred_mask, gt_mask):
    intersection = np.sum(pred_mask & gt_mask)
    dice = 2.0 * intersection / (np.sum(pred_mask) + np.sum(gt_mask))
    pred_boundary = get_boundary(pred_mask)
    gt_boundary = get_boundary(gt_mask)
    hd = max(max_distance_to_set(pred_boundary, gt_boundary),
             max_distance_to_set(gt_boundary, pred_boundary))
    pred_cc = count_connected_components(pred_mask)
    gt_cc = count_connected_components(gt_mask)
    mae_cc = abs(pred_cc - gt_cc)
    return dice, hd, mae_cc
```

## Common pitfalls

- Hausdorff loss computation is highly computationally expensive during training, significantly increasing training time.
- High-level priors like size loss degrade performance on datasets with large organ size variability or multi-label segmentation tasks.
- clDice optimizes topological skeletons, which can blur boundary details and worsen HD scores despite achieving good Dice accuracy.

## Evidence (verbatim from paper)

> The segmentation performances are compared via the 2 usual segmentation metrics: the Dice score $^{30}$  (DSC) presented in Table 2, the Hausdorff distance metric $^{31}$  (HD) presented in Table 3. In addition, we have computed the mean absolute error on the number of instances (connected components) presented in Table 4.

## Citation

```bibtex
@misc{eljurdi2022effect,
  title={Effect of Prior-based Losses on Segmentation Performance: A Benchmark},
  author={El Jurdi et al. (2022)},
  year={2022},
  note={arXiv:2201.02428}
}
```

- arXiv: 2201.02428