open source | AICell Lab

Hypha — Distributed Computing for AI-Powered Science

Mon, 01 Jun 2026 00:00:00 +0000

Modern science is increasingly built from many moving parts — AI models, large datasets, compute clusters, and laboratory instruments — that rarely live in the same place. Hypha is our open-source framework for connecting them. It lets researchers and AI agents call remote functions, services, and models as if they were local, organising everything into unified virtual workspaces.

Hypha is the backbone of much of what the lab builds: it powers cloud model serving in BioEngine, instant model testing in the BioImage Model Zoo, autonomous microscopy in Agent-Lens, and agent-ready biological data. Its companion libraries — hypha-rpc, hypha-core, and hypha-compute — make it easy to expose any Python or browser service to the network and to AI agents over standards like the Model Context Protocol.

Learn more in the documentation or try the public server at hypha.aicell.io.

ImageJ.JS — ImageJ in Your Browser

Thu, 01 May 2025 00:00:00 +0000

ImageJ.JS brings ImageJ — one of the most widely used scientific image-analysis tools — into the web browser. Powered by WebAssembly (via CheerpJ), it runs the full Java application with no installation: open it from any link, keep plugin support, work directly with local files, and get AI assistance for analysis. It is used daily by a global community of researchers and educators.

By removing the installation barrier, ImageJ.JS makes image analysis instantly shareable — ideal for teaching, reproducible workflows, and embedding interactive analysis in websites and notebooks. It integrates with the lab’s broader web-computing ecosystem around ImJoy and Hypha.

BioImage Model Zoo — FAIR AI Models for Microscopy

Sat, 01 Mar 2025 00:00:00 +0000

The BioImage Model Zoo is a community-driven, fully open resource where standardized, pre-trained deep-learning models can be shared, explored, tested directly in the browser, and deployed in many end-user tools — including ilastik, deepImageJ, QuPath, StarDist, ImJoy, and ZeroCostDL4Mic. A shared model standard makes these models cross-compatible, so a model contributed once can be reused everywhere.

The AICell Lab leads the user services and cloud infrastructure behind the Zoo: the model-upload and testing pipelines, and the BioEngine backend that runs the in-browser “test run” feature. Our goal is to make deep-learning methods for microscopy findable, accessible, interoperable, and reusable (FAIR) across the whole bioimaging ecosystem. This effort grew out of the now-completed AI4Life project and continues as a living community platform.