Scale Healthcare Machine Learning Through Expert Medical Image Annotation
Machine learning can change how care is delivered, but only when models are trained on data that clinical experts have labeled correctly. CapeStart annotates across ophthalmology, radiology, cardiology and laparoscopy, from single-class bounding boxes through to voxel-level segmentation, and delivers straight into your training pipeline, supported by our broader AI services for clinical data preparation, model validation and AI-assisted imaging.
Project output from our annotation team.
Top Row: Fluid segmentation on a retinal OCT b-scan, and knee segmentation on MRI.
Bottom Row: Coronary vessel labeling on CT angiography, and prostate segmentation on robotic surgical video
Four Practices, One Clinical Team
Every sample is project output from our own clinical team, with the modality, the annotation type and the classes named on each one.
Our ophthalmology team annotates anterior and posterior segment images for AI model development, model validation and clinical research.
Anterior segment work covers pterygium, bullous keratopathy and other corneal abnormalities. Posterior segment work covers the features behind grading in diabetic retinopathy, age-related macular degeneration and cystoid macular edema, from lesion detection through to pixel-level segmentation. We also annotate veterinary and preclinical imaging.
IMAGE TYPES AS-OCT, color fundus, OCT, fluorescein angiography, surgical video
ANNOTATION TYPES Segmentation, bounding boxes, boundary tracing, keypoints, polygons
SUPPORTS Model training and validation, clinical research, preclinical and veterinary studies
Top Row: Multi-class lesion annotation on color fundus, and fluid segmentation on a retinal OCT b-scan.
Bottom Row: Corneal pathology on anterior segment OCT, and anatomy and instrument annotation on cataract surgical video
Our radiology annotation team identifies and labels anatomical structures, lesions, abnormalities and clinically relevant findings across six modalities.
CT and MRI work covers multi-organ and voxel-level segmentation, lesion and tumor contouring, the vertebrae, the joints and the soft tissues. Ultrasound covers obstetric, fetal and cardiac studies, mammography covers masses, calcifications and BI-RADS scoring, and X-ray covers abnormality classification, lung findings and Cobb angle measurement, alongside surgical navigation.
IMAGE TYPES MRI, CT, PET/CT, ultrasound, mammography, X-ray
BODY REGIONS Head and neck, brain, chest, abdomen, pelvis, spine and vertebrae, joints, bone, breast
ANNOTATION TYPES 2D and 3D segmentation, bounding boxes, polygonal contours, landmarks, keypoints, measurements
Top Row: Knee segmentation on MRI, and renal mass segmentation on CT.
Bottom Row: Four chamber segmentation on fetal ultrasound, and a lesion carried across paired PET and CT.
Annotation of the aortic, mitral, tricuspid and pulmonary valves, including the anatomy of the valves, annulus, leaflets, chambers and vessels, with segmentation, landmarks and measurements for valve planning and interventional procedures.
Modality coverage runs across echocardiography, angiography and cardiac CT and MRI, from coronary vessel labeling on a volume rendered angiogram through to device position on a live fluoroscopic run. We also annotate ECG waveforms, intervals and rhythms, along with EP mapping data.
IMAGE TYPES Coronary CT angiography, echocardiography, fluoroscopy, cardiac CT and MRI, ECG and EP mapping
ANNOTATION TYPES Segmentation, landmarks, measurements, waveform and interval labeling
SUPPORTS Valve planning, interventional procedures, arrhythmia detection
Top Row: Coronary vessel labeling on CT angiography, and one valve study annotated across fluoroscopy, echo and a 3D reconstruction.
Bottom Row: Device labeling on a fluoroscopic frame, and the ECG waveform and interval scheme.
Anatomical structure identification, surgical instrument detection and tracking, tissue segmentation, surgical phase recognition and procedure assessment.
Anatomy is outlined class by class on both laparoscopic and robotic frames, covering the liver, gallbladder, cystic artery, cystic duct and prostate. Instruments are located, classified and tracked frame to frame, and procedure stages are recognized and assessed across a full operation.
IMAGE TYPES Laparoscopic video, robotic surgical video, still surgical frames
ANNOTATION TYPES Segmentation, polygons, bounding boxes, keypoints, classification, frame-to-frame tracking
SUPPORTS Instrument detection, surgical phase recognition, procedure assessment
Top Row: Prostate and tissue planes on robotic surgical video, and the hepatobiliary structures on laparoscopic video.
Bottom Row: The same classes carried with the instrument in frame, and instrument detection with keypoints.
Artificial Intelligence in Medical Imaging
We assist healthcare and medical AI organizations in building a data and validation foundation to develop reliable, clinically meaningful imaging AI.
Medical Image Annotation
- Image segmentation and classification
- Lesion and anatomical labeling
- Landmark identification and measurements
Clinical Data Preparation
- Medical image curation
- DICOM processing
AI Model Validation
- Clinical and ground-truth validation
- Model performance evaluation
- Edge-case and error analysis
- Bias assessment and inter-rater agreement
- AI output and annotation quality review
AI-assisted Imaging
- Automated disease and lesion detection
- Anatomical segmentation
- Image quality assessment
- Disease severity and risk stratification
- Quantitative imaging and longitudinal analysis
- Clinical decision support and report intelligence
Radiology
CT, MRI, X-ray, Ultrasound, PET/CT, Mammography
Ophthalmology
OCT, Fundus Photography, OCTA, FFA, Anterior Segment OCT
Applications
Oncology, Cardiovascular Imaging, Surgical Imaging, Digital Pathology, Veterinary Imaging
Medical image annotation
- Image segmentation and classification
- Lesion and anatomical labeling
- Landmark identification and measurements
Clinical data preparation
- Medical image curation
- DICOM processing
AI model validation
- Clinical and ground-truth validation
- Model performance evaluation
- Edge-case and error analysis
- Bias assessment and inter-rater agreement
- AI output and annotation quality review
AI-assisted imaging
- Automated disease and lesion detection
- Anatomical segmentation
- Image quality assessment
- Disease severity and risk stratification
- Quantitative imaging and longitudinal analysis
- Clinical decision support and report intelligence
Radiology
CT, MRI, X-ray, ultrasound, PET/CT, mammography
Ophthalmology
OCT, fundus photography, OCTA, FFA, anterior segment OCT
Applications
Oncology, cardiovascular imaging, surgical imaging, digital pathology, veterinary imaging
Image Annotation
- Bounding box: outline objects in images and video for object detection and computer vision.
- Polygon annotation: use multi-vertex contour tracing to preserve the exact shape of irregular objects.
- Key point annotation: mark key points for object pose estimation and motion tracking.
Segmentation and Masking
- Semantic segmentation: classify and label every pixel for high-quality segmentation datasets.
- Image masking: isolate objects, people, and backgrounds with detailed masks.
- Circle and ellipse annotation: label circular and elliptical objects with precise boundaries.
Text and Document Annotation
- Text classification: label intent, topics, and sentiment across text datasets.
- Named entity recognition: tag entities and domain-specific terminology in text.
- Document annotation: identify and label fields and regions across invoices, contracts, forms, and scanned business documents.
Video and Audio Annotation
- Video annotation: label and track objects, actions, and events across video frames.
- Video transcription and labeling: transcribe and label speech, events, actions, and timestamps.
- Audio transcription and labeling: transcribe and label speech, speakers, sounds, and key audio events.
Human expertise and AI
Medical AI requires more than algorithms. Our clinical and technical teams bring expertise in anatomy, pathology, imaging protocols, annotation workflows, quality control and AI validation to every project.
From Sample to Signed-Off Dataset
01
Define
Understand the clinical objective and the AI use case.
02
Curate
Prepare and structure high-quality imaging data.
03
Annotate
Apply clinically relevant labels, measurements and segmentations.
04
Validate
Perform quality and clinical review.
05
Deliver
Provide structured, AI-ready datasets and validation outputs.
Human Expertise and AI
Medical AI requires more than algorithms. Our clinical and technical teams bring expertise in anatomy, pathology, imaging protocols, annotation workflows, quality control and AI validation to every project.
Ready to Get Your Data Labeled?
Share a sample and your requirements. We will prepare a sample annotation, so you can review the quality before moving forward.
Talk to Our Experts
Tell us what you’re trying to achieve, and we’ll get back to you.