Whole-Slide Image Support
Handle gigapixel whole-slide images natively while preserving full-resolution pathology data.
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Pathology Annotation Platform
Pathology Annotation Platform for Whole-Slide Images
Annotate whole-slide images (WSI) for histopathology, tissue regions, cells, nuclei, biomarkers, and digital pathology AI with precise segmentation and expert review workflows.
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Pathology annotation features
Everything You Need for Complex Pathology Annotation
Work with whole-slide pathology images, move from tissue overview to cellular detail, organize labels with structured ontologies, and accelerate precise annotation with AI-assisted tools.
Deep Zoom & Multi-Resolution Viewing
Navigate seamlessly from whole-tissue overview to cellular-level detail across multiple resolution levels.
Annotation at Scale
Create, render, and manage thousands of cells, nuclei, regions, and other annotations across massive pathology slides.
Ontologies
Define nested pathology classes, attributes, relations, and slide-level properties for consistent annotation workflows.
Pixel-Perfect Segmentation
Precisely delineate cells, nuclei, tumors, tissue regions, and complex microscopic structures at pixel-level accuracy.
AI-Assisted Auto-Labeling
Accelerate pathology annotation with Find Similar, Magic Touch, and automated labeling tools for repetitive structures.
Human reviewAutomated labeling
Built for AI Data at Scale
Annotate complex pathology datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
10×
Faster pathology data annotation
Batch, Find Similar, Prompt Auto-Labeling, and automated workflows reduce repetitive manual pathology labeling.
90%
Automated pathology annotation
On average, 90% of pathology labels are pre-labeled automatically, then reviewed and refined by humans.
5×
Lower Training Data Costs
The cost per accepted label can be up to 5× lower as curation, annotation, and QA are automated.
Supported pathology annotation types
All pathology annotation types in one platform.
Create regions of interest, tissue segmentation masks, polygons, cell and nuclei instances, point markers, classifications, slide properties, and relations for pathology workflows.
01
Bounding Box / ROI
Mark rectangular regions of interest for tissue, tumor, lesion, cell cluster, or review areas.
02
Tissue Segmentation
Capture pixel-level tissue, tumor, lesion, necrosis, or biomarker regions with editable masks.
03
Polygon / Tissue Region
Outline irregular tissue regions, tumor margins, glands, and other structures with editable polygons.
04
Cell / Nuclei Instances
Label individual cells and nuclei as distinct instances for detection, counting, and morphology analysis.
05
Tissue Boundary / Polyline
Trace tissue boundaries, margins, vessels, and elongated structures with connected vertices.
06
Point / Cell Marker
Mark exact cell centers, nuclei, glands, and other microscopic landmarks on a slide.
07
Classification / Findings
Assign slide-, region-, or finding-level classes such as tissue type, grade, stain, or biomarker status.
08
Slide / Item Properties
Label properties that describe the complete slide or item, including source, quality, cohort, and project-specific attributes.
09
Relations
Connect findings and tissue regions to capture project-defined contextual relationships.
Pathology annotation quality assurance
Build quality into every pathology annotation.
Compare labels on the same tissue regions, check them against approved references, and resolve issues at the right magnification. Keep expert decisions connected to the original slide evidence.
Pathology Annotation Consensus
Compare independent labels on the same histology region. Identify omitted nuclei, differing tissue boundaries, and class disagreements for expert review.
Tissue-region evidenceAgreement checksReview disagreements
Quality Gate (Honeypot)
Check pathology annotations against approved references hidden from annotators. Evaluate supported boundaries and classes using the project's quality criteria.
Validation & Issue Resolution
Review missing properties and disputed nucleus or tissue boundaries in slide context. Correct the affected annotation and resolve feedback before approval.
Pathology QA Analytics
Track benchmark results, consensus outcomes, and review decisions across slide annotation tasks. Identify recurring issues and focus expert review on difficult regions.
Benchmark resultsReview outcomes
Pathology dataset curation & annotation workflows
Curate pathology data and automate annotation workflows.
Search, version, and inspect pathology datasets, connect AI models, and move annotations through review and approval.
Dataset Versions
Create and manage dataset versions as pathology data evolves. Track changes, assign work, and keep every version auditable and production-ready.
Semantic Search
Explore large pathology datasets through semantic understanding instead of manual filters. Find relevant cases and slide regions across defined conditions.
Embedding View
Visualize dataset structure, identify outliers and labeling issues, and improve pathology-data quality before training.
Integrated Workflows
Build workflows that connect models, annotation, review, and quality assurance in one continuous loop. Reduce handoffs and keep datasets moving from labeling to approval.
Bring Your Pathology Model
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world pathology datasets.
Solutions
Pathology annotation solutions for computational pathology
Build high-quality whole-slide and cellular datasets for tissue analysis, tumor modeling, and biomarker research.
Cell & Nuclei Analysis
Annotate individual cells, nuclei, and cellular structures across whole-slide pathology images for detection and quantification.
Explore cell & nuclei analysis
Tissue Segmentation
Label tissue types, compartments, and morphological regions across gigapixel histology slides.
Explore tissue segmentation
Tumor Region Identification
Define tumor boundaries, margins, necrotic regions, and surrounding tissue for computational pathology models.
Explore tumor region identification
IHC & Staining Annotation
Label positive and negative staining, cellular regions, and expert-defined staining categories for IHC training datasets.
Explore IHC & staining annotationQuestions, answered
Pathology Annotation Platform FAQs
Answers about WSI and histopathology annotation, cells, nuclei, tissue segmentation, digital pathology ontologies, quality workflows, custom models, and dataset operations in Unitlab.
Talk with the Unitlab teamWhat pathology annotation types does Unitlab support?+
Unitlab supports ROI boxes, tissue segmentation masks, polygons, cell and nuclei instances, point markers, boundaries, classifications, slide properties, and relations. Teams define labels and rules in reusable ontologies for consistent whole-slide image annotation.
Pathology annotation documentationHow does AI-assisted segmentation work in Unitlab?+
Use a built-in or integrated image model to generate a segmentation or polygon prediction, then review and refine the boundary before the annotation moves through quality review.
Auto-annotation documentationHow can reviewers inspect pathology regions consistently?+
Reviewers can inspect the full image and focused regions, compare labels against the ontology, leave issues and comments, and use annotation history to track what was reviewed or corrected.
Annotation review documentationHow do ontologies and properties work in pathology annotation?+
Nested ontologies define classes, classifications, properties, and relations. Class properties describe annotated regions, while slide or item properties capture context that applies to the complete image.
Properties and relations documentationHow does Unitlab manage pathology annotation quality and review?+
Configurable workflows route pathology tasks through annotation, review, rework, and approval. Instructions, assignments, comments, issues, annotation history, dataset versions, and releases keep quality decisions traceable for individual experts and enterprise teams.
Annotation and review documentationCan teams use their own AI models for pathology pre-labeling?
Yes. Unitlab can bring custom models into the annotation workflow to generate pre-labels and predictions. Annotators review and correct model output instead of starting from zero, while human approval remains part of the governed quality process.
Model integration documentationCan Unitlab curate, annotate, and version pathology datasets in one platform?
Yes. Teams can curate pathology data with metadata filters, semantic search, embeddings, similarity, and outlier discovery, then annotate selected samples, review results, and publish controlled dataset versions for reproducible AI development.
Dataset management documentationPATHOLOGY ANNOTATION PLATFORM
Build Production-Ready Pathology Datasets with Unitlab
Annotate, review, and manage complex pathology image data in one AI-assisted workspace. Move from raw images to governed datasets with model-assisted labeling and built-in quality control.
