Multi-modal versatility
Work with DICOM, MRI, CT, ultrasound, and 3D volumes in one place, slice by slice or in full 3D.
Medical
Build medical imaging models clinicians can trust.
A professional platform for annotating DICOM, MRI, CT, ultrasound, and histology data into precise training datasets for healthcare AI.
Applications
Medical image segmentation
Segment MRI, CT, and X-ray scans to identify organs, tissues, and abnormalities for accurate diagnosis and treatment planning.
Technical edge
AI-powered automation
Pre-label organs and structures, then interpolate contours across slices and procedure frames.
Quality control
Validation and multi-level clinical review catch missed findings before data reaches training.
Collaborative workflow
Route cases to annotators, reviewers, and clinical experts, each with the right permissions.
Customization & integration
Define custom labels for anatomy and pathology, and export in the formats your medical AI stack expects.
Enterprise-grade security
Protect patient data with strict confidentiality, HIPAA-aligned handling, and on-premise deployment.
Annotation types
Bounding box
Rectangular boxes that detect and classify organs, lesions, instruments, and anatomical structures in imaging.
Oriented bounding box
Rotated rectangles that capture the position and orientation of anatomical structures or tools in scans and procedures.
Polygon
Flexible shapes that outline tumors, organs, vessels, and tissue regions.
Points
Single points that mark anatomical landmarks, lesions, or references for measurement and modeling.
Lines & multilines
Trace vessels, nerve paths, incisions, and structural boundaries in imaging and procedural video.
Skeletons
Connected keypoints that model patient posture, joint movement, or instrument articulation for biomechanics analysis.
Cuboid
3D boxes that estimate the volume and position of organs, tumors, implants, and instruments.
3D point cloud
Label CT, MRI, and 3D scans to reconstruct accurate anatomical structures and spatial relationships.
Semantic segmentation
Pixel-level classification of images into tissues, organs, abnormalities, and background for diagnostic support.
Instance segmentation
Pixel-wise separation of individual structures or lesions in the same class for detailed analysis.
Bitmap
Precise raster masks for fine structures like micro-lesions or intricate tissue boundaries.
Mesh
3D surface reconstruction for digital twins of organs and bones, aiding surgical planning and simulation.
Custom
A mix of annotation methods built around your specific diagnostic or clinical task.
Put your medical data to work
Book a demo and we'll scope your medical imaging dataset with you.
Custom solution? hello@keylabs.ai
