Fiji: Introduction to Scientific Image Processing Training Course

Getting Started with the Fiji & ImageJ Ecosystem

• Understanding Fiji’s architecture: ImageJ core, plugins, and the update manager
• Installation, environment setup, and configuring automatic updates on startup
• Navigating the GUI: windows, toolbars, stack/series management, and keyboard shortcuts
• Supported scientific formats: TIFF, OME-TIFF, ND2, LIF, HDF5, and metadata standards
• Lab 1: Installing Fiji, configuring the update manager for auto-updates, and navigating a multi-channel fluorescence microscopy dataset

Core Image Processing & Quantitative Analysis

• Basic transformations: cropping, rotation, scaling, and channel splitting
• Filtering & enhancement: Gaussian, median, CLAHE, and noise reduction techniques
• Segmentation & feature extraction: thresholding, watershed, ROI Manager, and particle analysis
• Quantification: histogram analysis, color deconvolution, co-localization metrics, and statistical export
• Lab 2: Building a reproducible 2D/3D analysis pipeline on a sample cell imaging dataset and exporting structured measurement tables

Scripting, Automation & Multi-Language Workflows

• The Fiji Script Editor: writing, running, debugging, and parameterizing scripts
• Choosing the right language: Python (PyImageJ/ImgLib2), JavaScript (Nashorn), Groovy, and Beanshell
• Bridging Fiji with scientific computing ecosystems (NumPy, SciPy, pandas, scikit-image)
• Macro recording vs. scripting: when to use each and how to maintain clean, reusable code
• Lab 3: Writing a Python script to batch-process a z-stack, extract cell metrics, and automatically generate summary plots & CSV reports

Advanced Workflows: 3D Imaging, Stitching & Large Datasets

• Working with multi-dimensional bioimage data: virtual stacks, lazy loading, and memory management
• Tiled microscopy basics: acquisition patterns, tile numbering, and overlap handling
• Stitching large 3D datasets: using BigStitcher & TrakEM2 for registration and merging
• Performance optimization for hardware-constrained environments (RAM, GPU hints, cloud readiness)
• Lab 4: Registering and stitching a simulated tiled 3D microscopy dataset and optimizing memory usage for a >10GB z-stack

Extending Fiji: ImgLib2, Plugin Development & Deployment

• The ImgLib2 data model: N-dimensional arrays, views, and memory-efficient operations
• Building custom image processing algorithms using ImgLib2 & ImageJ2 APIs
• Plugin packaging: Maven structure, UI integration, and dependency management
• Sharing & deployment: creating local/global update sites, Docker containers, and reproducible research packages
• Collaborating across teams: standardizing parameters, version control for pipelines, and cross-lab sharing
• Lab 5: Developing a custom ImgLib2-based plugin, testing it locally, and publishing it to a shared update site

Reproducibility, Best Practices & Research Integration

• Capturing provenance: embedding scripts, parameters, and Fiji version info in results
• Metadata standards & FAIR principles for scientific image data
• Profiling, debugging, and troubleshooting common bioimage bottlenecks
• Community resources: ImageJ/Fiji documentation, forums, GitHub repos, and plugin ecosystem
• Final Project: Design, script, and document a complete image analysis workflow tailored to your research domain
• Customization Options: We offer tailored versions focused on:
  • Specific imaging modalities (confocal, super-resolution, electron microscopy, etc.)
  • Domain-specific pipelines (cell counting, colocalization, morphometrics, etc.)
  • Integration with existing lab infrastructure (Slurm, AWS, local HPC, or OME-TIFF archives)