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

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

This manual documents napari-vipp 0.13.0a1, available as an alpha pre-release from GitHub and PyPI. Its immutable v0.13.0a1 tag resolves to 7520a5bb3ea9fe296bb231c63d1598b833ac10f6, which passed the complete package and cross-platform test matrix. Final artifact hashes are recorded in the release notes. 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.

0.13 workflow and compute compatibility

0.13.0a1 writes workflow schema 4. A valid schema-3 workflow loads with an explicit CPU compute request, while schemas 1 and 2 remain rejected. Cached results are not saved in workflow JSON and generated Python is pinned to its creator version. Read the 0.13.0a1 release notes before upgrading and revalidate calculated results afterward.

Batch configs and manifests are version 3. Version-1 configs load with an explicit CPU request; version-2 configs keep their saved compute request. Both older versions have no source-axis declaration until reviewed and saved as version 3. For ordinary TIFF collections, review the Batch workspace's Image stack choice before treating generic pages as Z.

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 want to use my GPUInstall the optional stack, choose a compute policy, benchmark safely, and verify what actually ran. I need defensible resultsChoose dimensionality, tune on representative data, validate, and record what must be reported. I know what I needSearch all 112 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"]

Quick links

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
Choose and verify CPU/GPU execution	CPU and GPU compute
Review everything changed in 0.13	0.13.0a1 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.