Ground-truth 3D data for robust AI models
Alpenglow generates rigorously annotated, multi-modal 3D datasets at scale so your models, biomarkers, and decisions align with biological reality.
What we do
Alpenglow’s whole-tissue 3D light-sheet imaging platform produces high-resolution 3D microscope images that capture architectural context, cellular detail, and rare features not visible with current clinical imaging modalities. Alpenglow produces 10X the data at 1/10 the cost of conventional digital imaging technologies
Whole-specimen coverage captures complete cross-sections, eliminating the uncertainty produced by traditional digital pathology, which examines only a few slices of tissue.
Non-destructive, volumetric imaging preserves true tissue architecture.
Cell-level fidelity enables the precise definition of boundaries and phenotypes.
Rare feature visibility surfaces subtle events that 2D can miss.
Multi-modal alignment enables labels and analyses to co-register in the same volume.
Auditable datasets with versioned volumes and annotations.
Establishing a new Gold Standard in Ground Truth for AI
Train and validate models against real biology, not data inferred from a few slices.
Improve robustness across diverse specimens and edge cases.
Create benchmark data sets that support clinical and regulatory evaluation.
Why it matters for AI and diagnostics
Multi-Modal Annotation Framework
Our ground truth encompasses comprehensive spatial biology annotations that span multiple data modalities and biological scales. Cell segmentation forms the foundation, with precise boundary delineation of individual cells and subcellular compartments.
Marker co-localization
Mapping across fluorescent channels to identify protein interactions and cellular states.
Spatial neighborhood analysis
Understanding tissue architecture and cellular communication patterns.
Molecular expression profiling
Integration of gene and protein expression data with spatial context.
This multi-modal approach ensures our ground truth captures the full complexity of tissue biology, from molecular signals to organ-level organization, providing AI models with the comprehensive data needed for accurate biological understanding.
Our Systematic Ground Truth Generation Workflow
01
Sample Preparation & Controls
Standardized protocols ensure consistent tissue preservation and staining quality. Positive and negative controls validate every batch, while systematic documentation tracks sample provenance and processing conditions.
02
High-Resolution Imaging
Gold-standard imaging protocols capture tissue architecture at sub-cellular resolution. Multi-channel fluorescence imaging preserves spatial relationships while maintaining quantitative accuracy across imaging sessions.
03
Expert Manual Annotation
Board-certified pathologists and imaging scientists perform detailed annotations using specialized software. Each annotation undergoes peer review to ensure accuracy and consistency with established biological knowledge.
04
Quality Control & Validation
Rigorous QC protocols include inter-annotator agreement metrics, systematic bias detection, and continuous refinement processes. Version control systems track all changes and maintain annotation provenance.
Train and validate models against real biology, not inferred slices.
Improve robustness across diverse specimens and create benchmark sets that support clinical and regulatory evaluation.