Medical Imaging Annotation at Scale
Every modality. Every subspecialty. Physician-grade accuracy from radiology to pathology. DICOM-native workflows that integrate directly into your ML pipeline.
Imaging Modalities
CT Scans
Multi-slice annotation for lung nodule detection, liver lesion segmentation, brain hemorrhage classification, and abdominal organ delineation across axial, coronal, and sagittal planes.
MRI
Brain structure segmentation, cardiac function analysis, musculoskeletal injury grading, and tumor volumetry across T1, T2, FLAIR, and contrast-enhanced sequences.
X-ray
Chest pathology classification, fracture detection and grading, dental panoramic annotation, foreign body localization, and line/tube placement verification.
Ultrasound
Obstetric biometry, cardiac echocardiography measurements, abdominal organ assessment, and thyroid nodule characterization with TI-RADS scoring.
PET/SPECT
Oncology staging with SUV quantification, metabolic activity mapping, neurological tracer distribution, and fusion annotation with co-registered CT or MRI.
Mammography
Mass detection and characterization, calcification classification, breast density assessment, and BI-RADS category assignment by fellowship-trained breast imagers.
Digital Pathology
Whole slide image (WSI) annotation, cytology cell classification, immunohistochemistry (IHC) scoring, tumor grading, and mitotic figure detection at 40x magnification.
Fundoscopy
Retinal vessel segmentation, diabetic retinopathy grading, glaucoma optic disc analysis, age-related macular degeneration (AMD) classification, and drusen quantification.
Annotation Types
- ✓2D bounding boxes, polygons, and polylines
- ✓Semantic and instance segmentation
- ✓3D volumetric annotation across slices
- ✓Keypoint and landmark detection
- ✓Classification and grading labels
- ✓Temporal annotation for video and cine sequences
Data Formats
Input
DICOM, NIfTI, SVS, NDPI, PNG, JPEG, TIFF
Output
COCO, Pascal VOC, YOLO, custom JSON/XML
Integration
RESTful API, S3 bucket sync, webhook notifications
Why LabelCore for Medical Imaging
Subspecialty Matching
Every imaging study is annotated by a physician who reads that modality professionally. Neuroradiologists for brain MRI. Breast imagers for mammography. Pathologists for WSI. Clinical judgment is the annotation.
DICOM-Native
We work with DICOM files directly: no lossy PNG conversion, no metadata loss, no windowing errors. Our platform preserves the full bit depth, window/level settings, and spatial metadata your models need.
Regulatory-Ready
Gold Standard annotations meet the evidentiary requirements for FDA 510(k) and De Novo submissions. Full audit trails, annotator credentials on record, and inter-annotator agreement metrics included with every delivery.
Scale Without Compromise
From 1,000 images to 10 million. Our parallel annotation teams maintain quality at volume, with each team calibrated against the same gold-standard reference set with continuous inter-rater reliability monitoring.
Quality Tiers
| Tier | Accuracy | Method | Best For |
|---|---|---|---|
| Gold Standard | 99%+ | Physician-only, dual review | FDA submissions, clinical validation, peer-reviewed research |
| Hybrid Intelligence | 93%+ | AI pre-annotation + physician review | Production models, large-scale segmentation, detection pipelines |
| Ready Datasets | 85%+ | Pre-labeled, physician-sampled QA | Prototyping, feasibility studies, model benchmarking |
Explore by Specialty
Frequently Asked Questions
What medical imaging formats do you support?+
How are annotators matched to imaging modalities?+
What's the maximum dataset size?+
Do you support 3D volumetric annotation?+
What's the typical turnaround?+
Start Your Medical Imaging Project
Share your imaging modality, dataset size, and annotation requirements. We'll assign subspecialty-matched physicians and deliver a pilot batch within two weeks.