Collection

A collection is a software that encapsulate a set of bioimage components and/or workflows.

OrganoID

Collection
Description

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.

has topic
Live sample imaging
Bright field microscopy
Phase contrast microscopy
Machine learning
has function
Cell segmentation
Object feature extraction
Object tracking
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JIPipe: visual batch processing for ImageJ

Collection
Description

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.

has topic
Bioimage informatics
has function
Image processing
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Introduction to 3D Analysis with 3D ImageJ Suite

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.

GPU Accelerated Image Processing with CLIJ2

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.

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