Radiology Image Annotation Services for MRI, CT, X-ray, and Advanced Diagnostic AI

Radiology imaging datasets require precise and consistent annotation to train AI systems that support clinical decision making, triage, segmentation, and detection tasks. Radiology presents complex visual patterns, subtle findings, and anatomical variations that must be labeled with strict accuracy to reduce noise in training data.DataVLab provides radiology image annotation services designed for AI teams building models in diagnostic imaging, research institutions, and radiology technology companies. Annotators are trained to interpret radiological structures under detailed guidelines that specify class definitions, edge case management, labeling consistency rules, and region boundaries.We support MRI, CT, X-ray, PET, SPECT, DICOM sequences, mammography, neuroimaging, cardiac imaging, orthopedic radiology, and abdominal imaging. Annotation tasks include segmentation, region of interest labeling, detection bounding boxes, polygons, classification tags, anatomical landmarks, and multi class structures.Workflows include multi stage quality control with sampling, cross review, and refinement cycles. Complex cases are escalated through controlled review steps to maintain consistency. Sensitive radiology datasets can be processed within GDPR aligned workflows with optional EU only annotation.Our goal is to deliver highly reliable radiology datasets that support robust AI development while aligning with your scientific and clinical requirements.