Image Annotation

 Clinical annotation performed on native DICOM files without conversion, preserving the metadata that determines annotation accuracy −Hounsfield units, slice thickness, pixel spacing, patient orientation. Reviewed by credentialed clinical specialists with adjudication for ambiguous findings.

3D Cuboid Annotation encapsulates objects in 3D space, providing position, orientation, and dimensions. Our team precisely places these boxes around objects, crucial for tasks like autonomous driving.

Polygon annotation Point-by-point contours traced around irregular objects. Where a bounding box around a crop leaf, a parked car at an angle or a piece of damaged infrastructure would include large amounts of background, polygon annotation captures only the object −producing cleaner training signals for detection and segmentation models.

 Instance Segmentation Pixel-level masks that also distinguish individual objects of the same class. Necessary when counting matters −individual fruit on a plant, separate cells in a pathology slide, distinct vehicles in dense traffic.

Keypoint & Landmark Annotation Specific points marked on an object to capture pose, shape or orientation. Used for human pose estimation, facial landmark detection, animal behaviour analysis, and agricultural applications such as identifying stem and fruit attachment points for robotic harvesting.

Image Classification & Tagging Whole-image or multi-label classification −categorising images by content, quality, condition or attribute. Used in e-commerce catalogue structuring, content moderation and quality inspection.