Review-heavy by default
Diagnostic data can't tolerate sloppy labels. We run multiple review passes and consensus checks so ambiguous boundaries — where almost all the clinical risk lives — are resolved deliberately, not guessed.
Annotation · Sector
Annotation for medical & healthcare imaging.
Medical imaging demands a different kind of care — slower, more deliberate, review-heavy. A boundary that is a few pixels loose is not a cosmetic flaw here; it is a measurement error that changes tumour volume, a dose map, or a screening decision.
We bring the multi-pass QA discipline built on our agritech work to labelling the scans, slides and images that train diagnostic-support and screening AI — across X-ray, CT, MRI, ultrasound, dermoscopy and endoscopy, in DICOM and NIfTI. We treat medical data as sensitive by default and are growing a team with genuine medical-annotation experience.
Annotation types
What the work looks like.
Organ & lesion segmentation
Pixel-accurate masks and contours across CT/MRI slices and 2D modalities — the basis for volume, growth-rate and radiotherapy-planning measurements.
Radiology detection
Bounding boxes and RECIST-style measurements for findings on chest X-ray, CT and MRI — fast localisation before fine masking.
Pathology & cell work
Cell and nuclei counting, region labelling on histopathology slides, and instance masks that keep each object distinct.
Landmarks & classification
Anatomical keypoints for measurement and alignment, plus image-level classification — with multi-reader consensus on ambiguous cases.
Coverage
What we annotate.
Organ segmentation
Lesion masks
Radiology boxes
RECIST measurements
Cell / nuclei counting
Pathology regions
Anatomical keypoints
Multi-reviewer consensus
Where it's used
How it's used.
Privacy-conscious handling
We treat medical data as sensitive from the outset: controlled access, on-premise storage where required, and clear de-identification and handling agreements before a project begins.
Foundation-model ready
As promptable models like SAM and MedSAM move into the loop, expert review matters more, not less — we're set up for model-assisted labelling with human correction on every image.
We are expanding into medical imaging and welcome pilot datasets — a small calibration set lets us align on your protocol and prove the QA before scaling.
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