Can you recommend a polygon annotation tool for medical image annotation with pixel-level precision?

Yes — for medical image annotation with polygon-level / pixel-level precision, the best options depend on whether you need open-source, clinical workflow support, or research-grade flexibility.

Good recommendations

1. 3D Slicer

• Best for: medical imaging research and clinical-style workflows
• Why: It supports DICOM, volumetric images, segmentation tools, and very precise contour/polygon annotation
• Pros: free, open-source, widely used in medical imaging
• Cons: steeper learning curve than simple web annotation tools

2. ITK-SNAP

• Best for: semi-automatic and manual segmentation of medical images
• Why: Excellent for precise contouring on CT/MRI slices
• Pros: very good for pixel-accurate annotations, intuitive for segmentation
• Cons: more segmentation-focused than general-purpose polygon labeling

3. CVAT

• Best for: teams that want a web-based annotation platform
• Why: Supports polygons and masks, collaboration, review workflows
• Pros: browser-based, scalable, good for ML dataset creation
• Cons: not as specialized for medical DICOM workflows unless customized

4. Labelbox / Supervisely

• Best for: enterprise or team-based labeling
• Why: Strong annotation interfaces, collaboration, QA
• Pros: polished UI, workflow management
• Cons: paid, medical imaging support may depend on setup

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If you want the most precise medical-specific choice

• 2D slice annotation: ITK-SNAP
• Full medical imaging segmentation suite: 3D Slicer
• Multi-user dataset labeling: CVAT

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My practical recommendation

If you need polygon annotations with pixel-level precision on medical scans, I’d start with:

• ITK-SNAP for manual precision segmentation
• 3D Slicer if you need more advanced medical imaging tools
• CVAT if your main goal is building an ML dataset with polygon masks

If you want, I can also recommend:

1. a free/open-source-only option list,
2. a web-based tool list, or
3. the best tool for CT, MRI, or pathology images.