seg_mean_surface_distance — SEGMENTATION score op

• Data kinds: labels2d × labels2d → table

• Call: import fullseye as fs; fs.ledger.seg_mean_surface_distance(labels_pred, labels_true) -> 'Dict[str, float]' (to call the implementation directly, import segeval; segeval.seg_mean_surface_distance(labels_pred, labels_true) -> 'Dict[str, float]'; from the registry, opssegmentation.get("seg_mean_surface_distance"))

Usage

Average symmetric surface distance (ASSD): mean of the both-direction boundary distances, plus the RMS (seg_mean_surface_distance).

Boundaries follow the convention of _boundary. If either boundary is empty, ValueError.

Returns: assd, rms, mean_pred_to_true / mean_true_to_pred, n_pred_boundary / n_true_boundary.

Detailed usage guide

• halcon_segmentation family guide

Background guides (the physics and conventions behind this op)

• segmentation_scoring_and_worlds — Scoring segmentations and synthetic worlds with ground truth — which metric is blind to which failure

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• (none yet)

Ops the type connects to (they accept table as input)

class_ndim_norm

Same category (score)

seg_confusion_table · seg_dice_jaccard · seg_boundary_f · seg_hausdorff · seg_under_over_segmentation · seg_object_counts_match · seg_score_card

*Provenance: segeval.py — SEGMENTATION operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.