Object feature extraction

Feature detection
Image labeling

MATLAB app to characterize nanoparticles imaged with super-resolution microscopy. nanoFeatures will read text and csv files from the NIKON and ONI microscopes and from the ThunderSTORM Fiji plugin, then cluster the localizations and filter by size and sphericity and finally output nanoparticle features like size, aspect ratio, and number of localizations per cluster (total and for each channel).

GUI first tab to browse and input files, select input type and check extra filters if needed.

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

Figure 2 in OrganoSeg Scientific Reports publication

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.

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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.