napari-imagej: ImageJ ecosystem access within napari

The napari application has brought n-dimensional image browsing and analysis to the Python ecosystem. At the same time, the ImageJ software ecosystem, including ImageJ, ImageJ2, Fiji and thousands of community plugins, have been curated over decades, and are utilized by a highly active community of their own.

The napari-imagej plugin strives to unite these communities by providing access to an ImageJ2 instance within a napari widget. From this widget, users can launch the ImageJ user interface to run any ImageJ ecosystem functionality, and can additionally access ImageJ2 framework functionality directly.

napari-imagej handles the burden of data transfer between these two applications, enabling accessible, convenient, synergistic workflows.

Using ImageJ’s Analyze Particles routine within napari

Contents:

• Installation
  • Installing Within napari
  • Installing From Mamba (Recommended)
  • Installing From pip
  • Installing From Source
    • Using Mamba
    • Using pip
• Initialization
  • Starting napari-imagej
  • Starting the ImageJ GUI
• Configuration
  • Accessing napari-imagej settings
  • Configuring settings
    • ImageJ directory or endpoint
    • ImageJ base directory
    • include original ImageJ features
    • enable ImageJ GUI if possible
    • use active layer
    • JVM command line arguments
• Troubleshooting
  • napari-imagej does not appear in the Plugins menu of napari!
  • The search bar is disabled with the message “Initializing ImageJ…”
  • The ImageJ2 GUI button is greyed out!
  • The image dimension labels are wrong in ImageJ after transferring from napari
• Use cases
  • Tracking HeLa cell nuclei with TrackMate
    • TrackMate Setup
    • Preparing the Data
    • Running TrackMate
    • Processing tracks with napari-stracking
  • Viewing TrackMate Data in the napari Viewer
    • TrackMate XML
    • Obtaining sample data
    • Opening the data
  • Puncta Segmentation with SciJava Scripts (headless)
    • Configuration
    • The Code
    • Installing the script
    • Running the script
  • Image Processing with ImageJ Ops (headless)
    • Ops, explained
    • A simple gaussian Blur
• Developer’s Guide
  • Configuring a Mamba environment for development
  • Testing
  • Documentation
  • Formatting
  • Building distribution bundles
• napari-imagej Architecture
  • The napari-imagej Widget
    • Menu
    • Search Bar, Results Tree and Result Runner
  • Type Logic and Type Converters
  • The backing PyImageJ instance
• Benchmarking
  • Data
  • Timing in napari-imagej
  • Pure Java comparisons
  • Pure Python comparisons
  • Results