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Image credit: Lee Laboratory
Foundation AI for Live‑Cell Morphodynamics
Foundation AI for Live‑Cell Morphodynamics
ChrononAI turns raw time‑lapse microscopy into decision‑ready insights. Our foundation AI learns reusable temporal embeddings of live‑cell behavior—capturing migration, proliferation, and morphological change—so researchers can quantify phenotypes, compare conditions, and predict outcomes with confidence.
What we do
What we do
We provide microscope‑agnostic software that ingests live‑cell videos and delivers end‑to‑end analysis—segmentation, tracking, temporal representation learning, and phenotype discovery—together with intuitive visualization and reporting. The platform is assay‑agnostic, enabling consistent analysis across laboratories, instruments, and protocols.
How it works (Acquire → Understand → Decide)
How it works (Acquire → Understand → Decide)
Acquire — Capture time‑lapse sequences from USB microscopes or high‑content systems and import standard scientific formats (e.g., OME‑TIFF/NGFF).
Understand — Perform precise cell segmentation and multi‑object tracking; a time‑series Transformer encodes dynamic cell behavior into a compact temporal embedding for zero/few‑shot phenotyping.
Decide — Explore clusters, nearest neighbors, and exemplars, compute KPIs (e.g., wound closure rate, motility persistence), and export publication‑ready figures and reports.
Core capabilities
Core capabilities
Foundation AI for morphodynamics — Self‑supervised objectives (masked/contrastive) learn generalizable temporal features from diverse imaging conditions and cell lines.
Segmentation & tracking — High‑quality boundaries and stable identities optimized with metrics such as IoU/Dice and ID‑F1.
Phenotype Explorer — Interactive embedding maps, few‑shot labeling, and exemplar retrieval for rapid hypothesis generation.
Reporting & interoperability — One‑click PDFs/CSVs and seamless import of datasets from existing HCS stacks.
Image credit: Lee Laboratory
Why it’s different
Why it’s different
Most tools focus on snapshot or assay‑specific pipelines. ChrononAI centers on time‑dependent, generalizable representations that transfer across microscopes and labs, reducing per‑assay re‑engineering and enabling zero/few‑shot use cases. The platform is API‑first and designed to fit into existing imaging and analytics workflows.
Where it applies
Where it applies
Translational oncology — Dynamic drug‑response and invasion/motility readouts with simple, one‑click reports.
Discovery & MoA profiling — Dynamic phenotypic signatures that distinguish mechanisms, efficacy, and toxicity.
Bioprocess & cell therapy R&D — Non‑destructive morphodynamics as candidate CQAs and real‑time dashboards.
Full‑stack engineering power
Full‑stack engineering power
From data ingest to model serving and UI, ChrononAI is built end‑to‑end. We combine performant GPU pipelines (PyTorch → ONNX/TensorRT), robust back‑end services (API‑first REST/GraphQL), and modern front‑end frameworks (React/Next.js) to deliver speed, reliability, and seamless interoperability with Bio‑Formats/OME.
User‑friendly Cloud-based application/interface
User‑friendly Cloud-based application/interface
Science‑first design with sensible defaults: live video with overlays, one‑click reports, drag‑and‑drop imports, presets and keyboard shortcuts, plus tooltips and undo/redo. Power users can script workflows with a Python SDK/CLI; everyone gets a focused, uncluttered experience.
Scale when you need it. Containerized services and GPU‑backed inference (Kubernetes + Triton) enable secure, multi‑site collaboration, role‑based access, and shareable links—no local installs required. The same pipeline also runs on a single workstation for on‑prem scenarios.
FAQs
FAQs
What is label‑free imaging? Imaging without external dyes or genetic reporters—leveraging intrinsic contrast such as phase or scattering.
What does assay‑agnostic mean? One representation across multiple assays with only lightweight KPI and reporting adapters.
What is a foundation model here? A model trained on diverse time‑lapse data to learn reusable temporal embeddings that generalize across labs and conditions.
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