radiology-annotation

radiology-annotation is a Computer vision skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-annotation C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Design and document ROI/VOI/mask annotation that survives Radiology (RSNA) review — lesion-selection strategy (2D vs 3D, whole-tumour vs largest-slice vs peritumoral vs habitat vs multi-lesion), reader protocol (number, seniority, blinding, independent vs consensus, third-party adjudication), reproducibility (repeat annotation, ICC, Dice, Hausdorff, feature-stability filtering), and geometric integrity of masks against DICOM/NIfTI/DICOM-SEG/RTSTRUCT (spacing, origin, direction, slice order). Use when the user mentions ROI, VOI, mask, segmentation protocol, contouring, "标注", "勾画", "分割规范", inter-/intra-observer agreement, or needs the Methods paragraph for annotation. Produces an annotation SOP, a QC plan, and submission-ready Methods text. Never invents agreement values or reader details.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

819

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.