Cads Whole Body Ct Eval

cads-whole-body-ct-eval

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography — Murong Xu et al. (2025) (arXiv:2507.22953, 2025)

What this evaluates

Evaluates AI models on voxel-level segmentation of 167 whole-body anatomical structures from CT scans. It probes generalization across diverse imaging protocols, patient demographics, and pathological conditions, with a focus on clinical utility in radiation oncology.

Datasets

• CADS-dataset — total 22022; splits: train (-1), test (-1); repo https://github.com/murong-xu/CADS
• 18 Public Benchmark Datasets — total ?; splits: test (-1)

Metrics

• Dice coefficient (primary) — range: [0, 1]
  • Measures voxel-wise overlap between predicted and ground truth masks: 2 * |A ∩ B| / (|A| + |B|). Reported as a decimal or percentage.
• 95% Hausdorff Distance (HD95) — range: mm
  • Computes the 95th percentile of the maximum distance between corresponding points on the predicted and ground truth segmentation boundaries. Lower values indicate better boundary alignment.

Input / output format

Input: 3D CT scan volumes (typically aligned to 1.5mm isotropic resolution) covering anatomical regions from head to knees.

Output: Voxel-level segmentation masks for 167 predefined anatomical structures.

Scoring recipe

def compute_metrics(pred, gold):
    intersection = np.sum(pred & gold)
    dice = 2.0 * intersection / (np.sum(pred) + np.sum(gold))
    pred_bd = np.argwhere(pred)
    gold_bd = np.argwhere(gold)
    dists = cdist(pred_bd, gold_bd)
    hd95 = np.percentile(dists, 95) if len(dists) > 0 else 0.0
    return dice, hd95

Common pitfalls

• Relying on algorithm-generated ground truth labels (e.g., original TotalSegmentator annotations) which contain known inaccuracies for structures like ribs and vertebrae.
• Failing to stratify evaluation by annotation completeness (primary vs. secondary cohorts), which skews performance estimates in real-world clinical settings.
• Comparing models with different architectures without isolating dataset effects, leading to confounded generalization claims.

Evidence (verbatim from paper)

We use two primary metrics: Dice coefficient for volume overlap and 95% Hausdorff Distance (HD95) for boundary precision, while additional performance measures are detailed in Online Method Section 5.4.

Citation

@misc{xu2025cads,
  title={CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography},
  author={Murong Xu et al. (2025)},
  year={2025},
  note={arXiv:2507.22953}
}

• arXiv: 2507.22953