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This workflow describes a deep-learning based pipeline for reliable single-organoid segmentation and tracking in 2D+t high-resolution brightfield microscopy of mouse mammary epithelial organoids. The pipeline involves a four-layer U-Net to infer semantic segmentation predictions, adaptive morphological filtering to establish candidate organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking step to associate the corresponding organoid instances in time.

OrganoSeg is an open-source software that integrates segmentation, filtering, and analysis for breast-cancer spheroid and colon and colorectal-cancer organoid morphologies.

OrganoID is an image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids.

JIPipe is a visual programming language to realize code-free workflow building for ImageJ-based image analyses. GUI, graphical user interface. Currently, JIPipe unifies the functionality of over 1,000 ImageJ commands into a standardized interface, represented as nodes in the pipeline flow chart. The window-based data management implemented in ImageJ is replaced with a table-based model designed for batch processing. JIPipe is also available from within the ImageJ update service.

The 3D ImageJ Suite is a set of algorithms and tools (mostly ImageJ plugins) developed since 2010, originally for 3D analysis of fluorescence microscopy. Since then, the plugins have been widely used and cited more than 200 times in biological journals. In this presentation we will give a general introduction to the tools available in the 3D ImageJ Suite : filtering, 3D segmentation for spots and nuclei, and 3D analysis. A graphical interface to manage 3D objects, the 3DManager, was also developed and will be presented.

The NEUBIAS Academy at home about CLIJ2 gives an introduction to accelerated image processing using Graphics Processing Units (GPUs) in ImageJ/Fiji. Core concepts are explained as well as usage of the tools with the ImageJ Macro recorder and auto-completion in Fijis script editor. Furthermore, an outlook is provided of how the CLIJ project will develop in the coming years to provide long-term maintained access to GPU-acceleration in the Bio-Image Analysis context.

After the session you will be able to built your own CellProfiler pipeline, including:

• Image data import
• Object segmentation (e.g. detect nuclei in an image) using the modules "IdentifyPrimaryObjects" and "IdentifySecondaryObjects"
• Object feature measurements (e.g. measure size, shape and intensity of cells)
• Measurements export to a spreadsheet
• Creating and saving quality control images

• Introduction to Mathematical Morphology
• Morphological filtering of greyscale images
• Segmentation, e.g. focused on Watershed
• Image quantification (analysis of regions, texture analysis)

Workshop session about sharing and licensing materials such as [raw] data, manuscripts, code, slides. The slides cover the FAIR principles, terminology such as authors, copyright holders, licensees.

The one-day course is focused on processing microscopy images showing cells and nuclei. We will dive through segmenting objects, measuring their properties, managing measurements in tables and plotting results.

A 3h tutorial through bio-image data analysis and visualization using Fiji, pyimagej. clij, clesperanto, python and napari

A lecture about challenges and solutions for GPU-accelerating bio-image analysis workflows and running them in the cloud.

Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization. Fractal provides distributed workflows that convert TBs of image data into OME-Zarr files. The platform then processes the 3D image data by applying tasks like illumination correction, maximum intensity projection, 3D segmentation using cellpose and measurements using napari workflows.

Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization. Fractal provides distributed workflows that convert TBs of image data into OME-Zarr files. The platform then processes the 3D image data by applying tasks like illumination correction, maximum intensity projection, 3D segmentation using cellpose and measurements using napari workflows. The pyramidal OME-Zarr files enable interactive visualization in the napari viewer.
These slides are from an early demo of Fractal in November 2022

Recent years have shown a diversification of commonly used platforms for specific sub-domains of image analysis. Among the currently actively developed projects, napari has taken the stage as a versatile and powerful platform for the analysis of high-dimensional (3D, time-lapse) image data.

Cell tracking is a common bio-image analysis task. In this session we will learn about the basic principles behind cell tracking. We will go through cell segmentation, spot detection techniques such as Difference of Gaussian, linking, matching and will see how to do cell tracking in practice using TrackMate in Fiji.

This workflow applies a Stardist pre-trained model (versatile_fluo or versatile_HE) depending on the input images ie. uses both models for a dataset including both fluorescence (grayscale or RGB where all channels are equal) and H&E stained (RGB where channels are not equal) images.

This version uses tensorflow CPU version (See Dockerfile) to ensure compatibility with a larger number of computers. A GPU version should be possible by adapting the Dockerfile with tensorflow-gpu and/or nvidia-docker images.

This workflow processes a group of images containing cells with discernible nuclei and segments the nuclei and outputs a binary mask that show where nuclei were detected. It performs 2D nuclei segmentation using pre-trained nuclei segmentation models of Cellpose. And it was developed as a test workflow for Neubias BIAFLOWS Benchmarking tool.

MiNA is a simplified workflow for analyzing mitochondrial morphology using fluorescence images or 3D stacks in Fiji. The workflow makes use of ImageJ Ops, 3D Viewer, Skeletonize (2D/3D), Analyze Skeleton, and Ridge Detection.

It stitches 3D tiles from terabyte-size microscopy datasets. Stitching does not require any prior information on the actual positions of the tiles, sample fiducials, or conversion of raw TIFF images, and the stitched images can be explored instantly.

MosaicExplorerJ was specifically designed to process lightsheet microscopy datasets from optically cleared samples. It can handle multiple fluorescence channels, dual-side lightsheet illumination and dual-side camera detection.