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

Radiology image annotation labels medical imaging data from radiological modalities, primarily CT, X-ray, MRI, PET, and fluoroscopy, so that AI systems can learn to detect findings, segment anatomical structures, characterize lesions, and support radiologist workflows. It includes organ and structure segmentation (liver, lung, kidney, spleen, vertebrae, lymph nodes), abnormality detection and localization (nodules, masses, fractures, effusions, infiltrates, calcifications), lesion characterization (size, shape, density, margins, enhancement), and findings classification against established radiology reporting standards. Radiology annotation requires radiologists with the relevant subspecialty training for each anatomical region and pathology type.

CT annotation labels three-dimensional volumetric data from computed tomography scanning. Because CT provides cross-sectional imaging across the entire body, CT annotation typically involves multi-organ segmentation across many axial slices and pathology detection across a wide range of findings. Lung CT annotation for nodule detection is the most common AI-radiology application: annotators mark each nodule, characterize its morphology (solid, subsolid, ground-glass), measure its size, and apply Lung-RADS category. Chest CT annotation additionally covers pneumonia, pleural effusion, pericardial effusion, pulmonary embolism, and cardiac findings. CT annotation speed varies from 5 to 30 cases per radiologist hour depending on annotation complexity.

Chest X-ray annotation labels findings from the most common radiological examination, covering lung parenchymal findings (consolidation, atelectasis, nodule, effusion), cardiac silhouette assessment, mediastinal contour, and skeletal findings. Common annotation tasks include multi-label classification of chest X-ray findings (is consolidation present? is pleural effusion present?), bounding box annotation around findings for detection models, and segmentation of lung fields, cardiac silhouette, and diaphragm for normality baselines. Chest X-ray annotation by radiologists typically proceeds at 20 to 60 cases per hour for binary classification tasks and 5 to 15 cases per hour for detailed multi-finding bounding box annotation.

Radiology AI systems intended for clinical diagnostic support are medical devices under EU MDR and must carry CE marking. The EU AI Act additionally classifies these systems as high-risk under Annex I (safety components of medical devices requiring third-party conformity assessment). Training data must satisfy Article 10 data governance requirements with documented annotation methodology, annotator credentials (board certification, subspecialty), inter-annotator agreement metrics, and dataset representativeness evidence. The annotation documentation produced for training data is part of the clinical evidence package submitted for CE marking and becomes part of the post-market surveillance system documentation.

Radiology annotation quality depends on annotation guideline specificity, annotator expertise, and multi-reader validation. For critical applications (cancer detection, PE diagnosis), multi-reader annotation with adjudication is standard: three or more radiologists annotate each case independently, and a senior subspecialist adjudicates cases where readers disagree. Inter-reader agreement on Dice coefficient should exceed 0.85 for segmentation tasks and Fleiss kappa should exceed 0.75 for classification tasks. For the most clinically sensitive tasks, consensus annotation with real-time discussion among multiple radiologists produces higher ground truth quality than adjudicated disagreements, at the cost of substantially more radiologist time per case.

DataVLab provides radiology annotation across CT (lung, chest, abdomen, pelvis, musculoskeletal, head and neck), chest X-ray (multi-finding detection and classification), MRI (brain, body, cardiac, musculoskeletal), PET/CT fusion annotation (oncology, neurology), and fluoroscopy annotation for procedural guidance AI. We work with radiology AI companies, hospital digital health programs, pharmaceutical clinical trial imaging programs, and medical device manufacturers. All radiology annotation programs use board-certified radiologists with relevant subspecialty training. EU-based annotation with GDPR-compliant patient imaging data handling is available for European radiology AI programs.