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VIPP — Visual Image Processing Platform

See every decision in your analysis

Visual workflows for reproducible bioimage analysis.

Build bioimage-analysis workflows as connected graphs, inspect intermediate images and tables, then save the workflow for review, adaptation, or batch processing.

Start with a guided example Find a workflow

Napari with a docked VIPP label-cleanup workflow in the dark interface

A complete label-cleanup graph shown in context. For day-to-day authoring, enlarge or undock VIPP so the graph remains the primary work surface.

Alpha release: validate before interpreting

The current public baseline is napari-vipp 0.12.0a3. Interfaces, workflow files, and parameter defaults may change between alpha releases. Treat visual inspection, reference data, and domain review as part of the analysis—not as optional cleanup after it.

0.12 workflow compatibility

0.12.0a3 keeps workflow schema 3 and intentionally rejects schema 1 and 2. Valid 0.12.0a1 and 0.12.0a2 schema-3 workflows load structurally, but caches are not saved in workflow JSON and generated Python is pinned to its creator version. Read the 0.12.0a3 release notes before upgrading and revalidate calculated results afterward.

Choose your path

New to VIPPInstall the plugin, tour a finished graph, and build a small segmentation workflow. I have an analysis taskFollow recipes for segmentation, measurements, networks, colocalization, restoration, or batch runs. I need defensible resultsChoose dimensionality, tune on representative data, validate, and record what must be reported. I know what I needSearch all 108 nodes, bundled samples, example workflows, settings, formats, and compatibility notes.

What VIPP records—and what it does not

VIPP workflow JSON records the graph, node parameters, connections, layout, and selected workflow state. Where available, image state carries axes, scale, units, channel information, and operation history through compatible nodes. This supports inspection and repeat execution, but it does not by itself guarantee scientific reproducibility: input identity, software environment, batch bindings, reference annotations, exclusions, and validation evidence must also be retained.

flowchart LR
  A["Load representative data"] --> B["Build and tune graph"]
  B --> C["Inspect intermediate outputs"]
  C --> D["Validate against references"]
  D --> E["Freeze workflow and environment"]
  E --> F["Run batch and audit outputs"]
Goal Go to
Open a working example in five minutes Tour a finished workflow
Switch from synthetic data to your images Use your own images
Understand images, masks, labels, and tables Data types
Diagnose a workflow that suddenly gives different counts Common problems
Ask a question or report a reproducible problem Support routes
Prepare methods and provenance for a paper Report a VIPP analysis
Understand what changed in 0.12 0.12.0a3 release notes
Contribute a node or documentation fix Contributor guide

The application is developed in the napari-vipp repository. This site is the quick, searchable manual; slower teaching material can live in a separate course or book.