Medical Seg Eval

---
name: medical-seg-eval
description: Evaluates deep learning models for medical image segmentation by quantifying region overlap and boundary alignment between predicted masks and expert-annotated ground truth. Use when the user wants to benchmark on LA (Left Atrium), Pancreas CT, BraTS 2019, ACDC, or asks about evaluating this task. Reports DSC.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2207.14191
  bibtex_key: jiao2022ssl4mis
  confidence: high
---

# medical-seg-eval

> Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation — Jiao et al. (2022) (arXiv:2207.14191, 2022)

## What this evaluates

Evaluates deep learning models for medical image segmentation by quantifying region overlap and boundary alignment between predicted masks and expert-annotated ground truth.

## Datasets

- **LA (Left Atrium)** — total 100; splits: train (80), test (20)
- **Pancreas CT** — total 82; splits: train (62), test (20)
- **BraTS 2019** — total 335; splits: train (250), val (25), test (60)
- **ACDC** — total 100; splits: train (70), val (10), test (20)

## Metrics

- `DSC` **(primary)** — range: [0, 1]
  - Dice Similarity Coefficient measures region overlap: DSC = 2|G ∩ S| / (|G| + |S|), where G is ground truth and S is segmentation result.
- `IoU` — range: [0, 1]
  - Intersection over Union (Jaccard) measures overlap: IoU = |G ∩ S| / |G ∪ S|.
- `HD` — range: other
  - Hausdorff Distance measures maximum boundary error: HD(∂G, ∂S) = max(max_x∈∂G min_y∈∂S ||x-y||_2, max_x∈∂S min_y∈∂G ||x-y||_2).
- `95HD` — range: other
  - 95th percentile of the Hausdorff distances between boundary points, used to eliminate outlier influence.

## Input / output format

**Input**: 3D or 2D medical imaging volumes (MRI or CT) with isotropic or fixed resolution, often intensity-normalized or clipped to specific Hounsfield Unit ranges.

**Output**: Binary or multi-class segmentation masks/labels corresponding to the input volume.

## Scoring recipe

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

def compute_iou(G, S):
    intersection = np.sum(G & S)
    union = np.sum(G | S)
    return intersection / union if union > 0 else 0.0

def compute_hd(G, S):
    dists = cdist(G, S)
    return max(np.max(np.min(dists, axis=1)), np.max(np.min(dists, axis=0)))
```

## Common pitfalls

- DSC and IoU measure region overlap but ignore boundary errors, which can be critical for small lesions or thin structures.
- HD is highly sensitive to outliers; 95HD is preferred to mitigate the influence of small subsets of outlier boundary points.
- Semi-supervised splits vary across papers (e.g., 10% vs 20% labeled), making direct comparison difficult without standardized task settings.

## Evidence (verbatim from paper)

> For medical image segmentation tasks, Dice Similarity Coefficient (DSC) is a widely used metric to measure the region overlap ratio of the ground truth G and segmentation result S. Another similar metric IoU (or Jaccard) is used as an alternative for the evaluation. These two metrics are defined as follows: DSC = 2|G ∩ S|/(|G| + |S|), IoU = |G ∩ S|/|G ∪ S|.

## Citation

```bibtex
@misc{jiao2022ssl4mis,
  title={Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation},
  author={Jiao et al. (2022)},
  year={2022},
  note={arXiv:2207.14191}
}
```

- arXiv: 2207.14191