OpenHCS

OpenHCS

Turn high-content microscopy images into reproducible measurements\ One reviewable workflow across the GUI, Python, CellProfiler, and local agents

OpenHCS is designed for imaging scientists and research software teams running high-content studies where many wells, sites, channels, Z planes, or time points must be analysed consistently. Source selection, processing steps, and result definitions stay together in one validated pipeline instead of being split across interface-only state, scripts, and automation.

It is a good fit when a workflow must remain reviewable across visual editing, code, and automation. The same pipeline can be edited in the desktop GUI or as Python, imported from supported CellProfiler .cppipe files, and built or reviewed through the local MCP surface.

Install

Windows installer · macOS installer · Installation options

The graphical installers set up an isolated CPU-safe desktop environment with the OpenHCS GUI, CellProfiler compatibility, local MCP server, Napari, Fiji/ImageJ, and Bio-Formats. GPU libraries remain optional.

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See OpenHCS in use

Browse the UI and viewer gallery · Watch an agent build, debug, run, and inspect a workflow

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OpenHCS processes large microscopy datasets with a compile-then-execute architecture. Pipelines are validated across the selected execution axes *before* processing starts, preventing late failures after expensive work. Design pipelines in the GUI, export to Python, edit as code, and re-import — switching between visual and programmatic workflows. The local MCP exposes that same workflow model to supported agents, so agent-authored pipelines remain visible, editable, and reviewable in the GUI and generated Python.

graph LR

subgraph Sources IX[ImageXpress] OP[Opera Phenix] BF[Bio-Formats] OM[OMERO] end

subgraph OpenHCS Platform PD["Pipeline Designer (GUI ⇄ Code ⇄ Agent)"] CO["Typed Compiler (resolve + validate)"] EX["Bounded Worker Executor (well scheduling · multi-GPU)"] FN["Registry-Discovered Functions scikit-image · CuPy · pyclesperanto PyTorch · JAX · TF · CuCIM · custom"] PS["PolyStore (Memory ↔ Disk ↔ Zarr ↔ Stream)"] end

subgraph Viewers NA[Napari] FJ[Fiji/ImageJ] end

IX --> PD OP --> PD BF --> PD OM --> PD PD --> CO --> EX EX --> FN --> PS PS --> NA PS --> FJ

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⚡ Key Capabilities

🛡️ Compile-Time Validation

Configuration is resolved once into step snapshots and a compilation session. Typed plans then validate sources, artifacts, materialization, memory contracts, and worker requirements before execution begins. Errors surface immediately, not after hours of processing.

🔄 Bidirectional GUI ↔ Code

Design pipelines visually, export as executable Python, edit in your IDE, re-import to the GUI. Code generation works at any scope level — function patterns, individual steps, pipeline configs, full orchestrator scripts — any window holding objects can generate and re-import code.

🧠 Agent-Assisted Workflows

Give a supported MCP client a microscopy folder or plate and an analysis goal. It can inspect the connected execution server's functions, build and validate a typed pipeline, run it, inspect results in OpenHCS or a viewer, and revise the generated Python.

⚡ Multiprocessing & GPU Acceleration

Bounded worker lanes use ProcessPoolExecutor by default, with deterministic well assignment and sequential processing inside each lane. Compiled callable contracts select framework-local GPU devices independently; single-worker and debugging configurations can use inline or threaded execution.

🔌 Any Python Function

Register any Python function by decorating it with @numpy, @cupy, @pyclesperanto, @torch, or another memory-type decorator. Custom functions receive contract validation, UI integration, multiprocessing-safe import identity, and the same server-owned catalog projection as built-in functions. Persisted functions live in the platform-specific OpenHCS user-data directory.

📊 Results Materialization

Callable and module artifact contracts declare semantic outputs independently of Python argument names. The artifact graph and materialization plans route images, measurements, object labels, relationships, tables, and files to their configured stores and exporters.

🔬 Process-Isolated Napari & Fiji

Stream images to Napari and Fiji/ImageJ in real time during pipeline execution. OpenHCS StreamingConfig declarations and viewer adapters own identity, display, and persistence policy. PolyStore builds generic storage and streaming payloads; ZMQRuntime supplies process-isolated transport, readiness, acknowledgments, and lifecycle.

🪟 Live Cross-Window Updates

Edit a value in GlobalPipelineConfig — watch it propagate in real-time to PipelineConfig and StepConfig windows. Dual-axis resolution (context hierarchy × class MRO) with scope isolation per orchestrator.

🧬 CellProfiler Pipeline Import

Open .cppipe files in the desktop application or lower them from Python into ordinary PipelineConfig and FunctionStep declarations. Named images, objects, measurements, relationships, and exports use the same typed compiler and runtime as native OpenHCS pipelines. The source-backed Official30 suite continuously exercises 30 pipelines from CellProfiler examples, tutorials, and benchmark supplements under explicit equivalence policies.

🤖 MCP Agent Automation

Use the local stdio MCP server with ChatGPT desktop, Codex, Claude Desktop, and other supported clients, or deploy the separately secured HTTP surface. The graphical installers register detected local clients automatically. Capability profiles, schemas, knowledge, UI attachment, authoring, execution, runtime inspection, viewer review, and governed custom-function registration are projected from typed authorities rather than duplicated tool lists.

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🧩 The OpenHCS Ecosystem

OpenHCS is built on 8 purpose-extracted, separately published libraries — each solving a general problem and all composed into one platform:

graph TD

OH["OpenHCS Platform (domain wiring + pipelines)"]

OH --> OS["ObjectState (config)"] OH --> AB["ArrayBridge (arrays)"] OH --> PS["PolyStore (I/O + streaming)"] OH --> ZR["ZMQRuntime (exec)"] OH --> QR["PyQT-reactive (Qt forms)"] OH --> FB["formbridge (form model + UI bridge)"] QR --> FB

OS --> PI["python-introspect (signatures)"] OH --> MR["metaclass-registry (plugins)"] OH --> PC["pycodify (serialization)"]

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🔬 Microscope & Function Support

Image Sources

Source handlers are auto-detected and extensible through metaclass-registry.

Functions — Automatic Discovery

The connected execution server owns the available catalog, so remote GPU and custom-function availability is reflected without a manually maintained list. ArrayBridge provides compatible memory conversion, including zero-copy paths where supported.

Processing domains: image preprocessing · segmentation · cell counting · stitching (MIST + Ashlar GPU) · neurite tracing · morphology · measurements

Dimensionality is function-defined rather than a global mode: true volumetric segmentation and measurement routes coexist with plane-local functions, whose labels are not silently stitched across Z. See the [dimensionality and measurement capabil