Our methodology is designed to solve foundational problems in quantitative biomedical imaging by explicitly identifying and encoding the prior knowledge required for generalization.
We construct systems around mathematically general representations that span 2D, 3D, and 4D imaging data. These representations enable consistent handling of diverse modalities without task-specific re-engineering.
This approach allows rapid customization for new domains while preserving stability, interpretability, and reproducibility.
Automated inference alone is insufficient for high-stakes biomedical applications. Our pipelines integrate AI-driven processing with quantitative QA/QC agents and expert human review.
Custom ground-truth data—generalizable across species and modalities—anchors this process, ensuring measurable reliability rather than assumed correctness.
All core methodology is derived from the open-source ANTsX ecosystem, grounded in decades of peer-reviewed research.
For enterprise deployment, we extend this foundation with enforced provenance, versioning, security controls, privacy guarantees, and population-scale execution infrastructure—without compromising scientific transparency.