Medical Annotation

Annotate DICOM and medical imaging with synchronized multiplanar views, clinical ontologies, timelines, tracking, and AI-assisted segmentation.

Unitlab’s medical Workbench keeps a study together across axial, coronal, sagittal, 3D, and sequence views. Specialists can create precise findings, inspect them from synchronized perspectives, apply structured clinical properties, and route work through review without fragmenting one case into unrelated images.

Use this guide when: you are labeling DICOM studies, volumetric scans, medical sequences, lesions, organs, findings, measurements, or clinical imaging datasets.

See medical annotation in action

The demo shows synchronized medical views and annotation assistance inside the current clinical Workbench.

Open the demo in a new tab.

Before you begin

1. Create or select a project whose data and ontology match this modality.

2. Confirm the project instructions define the unit of annotation, boundary or timing policy, required properties, and review route.

3. Open the project and enter the assigned item from the project data view or queue. The Workbench loads the modality-native editor inside the shared Unitlab shell.

See Annotation Workbench for navigation, saving, item state, comments, issues, and workflow actions.

Understand the medical work surface

The medical Workbench combines synchronized slice position, native medical views, 3D context, annotation geometry, sequence timelines, clinical ontology values, and workflow actions. A change created in the active view remains tied to the same study and can be inspected from sibling views.

Synchronized axial, coronal, sagittal, and 3D DICOM views

Multiple clinical views preserve one finding and one study context while supporting specialist inspection.

Supported annotation model

Annotation type	Use it for
Segmentation mask	Organs, tissues, tumors, lesions, and other pixel- or voxel-aligned regions.
Polygon or contour	Editable boundaries on a slice or supported projection.
Bounding box	Coarse localization of a finding or region of interest.
Point or keypoint	Landmarks, centers, and precise anatomical locations.
Line or measurement	Distances, axes, and elongated structures where supported by the project.
Temporal or sequence label	A finding or state on a medical sequence timeline.
Clinical property	Structured values attached to a finding or anatomy class.
Item Property or relation	Study-level context and governed connections between findings.

Clinical label policy must separate what is visible, what is inferred, and what is not assessable. Geometry agreement alone does not guarantee agreement on diagnosis, certainty, or acquisition quality.

Multiplanar editing and synchronized views

Choose the active view for annotation, navigate to the relevant slice, and inspect the same finding in synchronized orthogonal and 3D context. Use view switching to detect leakage into adjacent anatomy, missed extent, or a contour that looks plausible in one plane but inconsistent in another.

Multiplanar medical annotation with synchronized slice position and geometry

Edits remain part of one study while specialists inspect the finding from complementary planes.

Clinical ontologies and properties

Use nested classes, required properties, relations, and Item Properties to encode anatomy, finding type, certainty, grade, acquisition context, and project-specific clinical values. Keep visible morphology separate from inferred diagnosis when the project requires that distinction.

Clinical ontology with lesion and liver classes, attributes, relations, and temporal segmentation

The ontology carries clinical meaning that geometry alone cannot represent.

AI-assisted segmentation and sequence tracking

Use supported model assistance to propose tissue or lesion masks, then refine them in the native editor and inspect the result across views. For medical sequences, auto-tracking can propagate contours across time or frames; correct the first drift and review every transition. Model output remains a proposal until specialist approval.

AI-assisted medical segmentation with editable tissue masks

Model assistance accelerates delineation while clinical review controls the final label.

Annotate one production item

1

1. Open the study

Confirm the patient-safe study identifier, series, modality, orientation, ontology, instructions, and current review stage.

2

2. Synchronize the views

Navigate to the target slice or frame and inspect the finding in the available axial, coronal, sagittal, 3D, or sequence context.

3

3. Create the finding

Choose the class and geometry, then draw the mask, contour, box, point, or measurement in the active view.

4

4. Inspect extent and continuity

Move through adjacent slices or frames and compare sibling views. Correct leakage, discontinuity, inconsistent identity, or missed extent.

5

5. Complete clinical structure

Fill required clinical properties, study Item Properties, relations, and temporal values without inferring unsupported facts.

6

6. Specialist review and route

Review full-study context, validation, comments, and issue history; save and route to the configured medical review or approval stage.

Quality review

Review focus	What to check
Study identity	All labels must remain attached to the correct study, series, slice, and frame.
Cross-view consistency	Inspect the finding in synchronized views, not only the plane where it was drawn.
Clinical semantics	Separate observation, certainty, diagnosis, and non-assessable values according to policy.
Segmentation extent	Check adjacent slices, small islands, holes, leakage, and partial-volume boundaries.
Specialist ownership	Use qualified reviewers and explicit escalation for ambiguous clinical findings.

A saved annotation is not automatically a production-ready annotation. Required values, boundary or timing policy, cross-item consistency, and the configured review stage still apply.

Move from labels to governed data

Medical outputs should preserve study and series identity, slice or frame position, geometry, clinical properties, relations, reviewer provenance, and ontology version. Use controlled dataset versions and releases for training or validation cohorts, and apply organizational privacy, access, and deployment requirements to protected health information.

Integrated Unitlab workflow connecting model assistance, annotation, review, and quality assurance

Use workflows to keep model output, human correction, review, and approval in one traceable operating path.

Next steps

• Use AI-assisted Annotation to calibrate interactive and batch assistance.

• Use Multimodal overview when related files or views must stay in one task.

• Curate difficult cases and review cohorts in Data curation.

• Read the current medical annotation product overview for the feature overview and current media.