Real-time anatomy recognition in the robotic OR, built on surgeon-reviewed labels.
Read the story- annotated surgical frames
- 1,500+
- prototype on the robot console
- Live
CVAT for Healthcare & Medical Imaging AI
CVAT turns surgical video, medical scans, and microscopy into validated training data for detection, segmentation, and clinical decision support, trusted by teams like UIC Urology.
Use cases
Turn Medical Imagery Into Expert-Verified Training Data
Surgical video and anatomy recognition
Segment anatomy and instruments frame by frame in surgical video.
Radiology and medical scans
Outline organs, lesions, and nodules across X-ray, CT, and MRI.
Pathology and cell microscopy
Count cells, nuclei, and dividing cells across every slide and well.
Dental imaging
Label teeth, restorations, and caries on dental X-rays.
Retinal and eye imaging
Mark the optic disc, vessels, and lesions in fundus photos.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect your de-identified imaging archive, existing labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/chest-xray-v2
Search sourcesAdd source
SourceStatus
1s3://surgical-video/deidentifiedSyncing…
2gs://radiology-archive/cxr-2026Syncing…
3azure://pathology/slides-q3Syncing…
4labels_v1 · COCO 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7annotation_protocol_v2.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every frame and scan. Your clinicians review and fix what it misses.
Book a demoJobs/Study 0418
lung #1
lung #2
trachea #3
heart #4
nodule #5
Ship with Confidence
Catch label errors before they reach training
Every job is scored against ground truth, so only clean data reaches training.
Book a demoTasks/Quality control
Mean annotation quality
98.4%
Target 95%
Honeypots
CVAT is how ML teams ship medical AI.
