Fluorescent immunophenotyping uses fluorescently-conjugated antibodies to identify, characterize and quantify distinct subpopulations of cells within heterogeneous single-cell populations, either in the context of tissue (using fluorescence and imaging microscopy) or in a single-cell suspension (using multiparameter imaging microscopy, imaging cytometry, and/or flow cytometry).
Allows user to explore high-content microscopy images. PhenoRipper assists users in comparison of images and based on similarity of image phenotypes. It uses cluster analysis to identify superblock types, representing the most common block type co-occurrence patterns. It profiles each image by the frequency of occurrence of superblock types.
A software for biological image-based classification, data exploration, and visualization designed for biologists and data scientists. CellProfiler Analyst builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).
Represents a generic novelty detection and deep learning framework which can enables sensitive and accurate cellular phenotype detection. CellCognition Explorer serves for integrated data analysis from raw images to phenotype scores and consists of two programs: (1) the principal CellCognition Explorer permits interactive data visualization tools and the possibility to perform versatile analysis workflows, (2) and the CellCognition Deep Learning Module which is a separate program for graphics processing unit accelerated high-performance computing of deep learning features.
A user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction.
Assists users to analyze three-dimensional microglia morphology in mammalian brains. MIC-MAC is a program that enables automatic 3D-morphology characterization and classification of thousands of individual microglia. This tool can capture morphological heterogeneity of microglia in large brain sections immunostained for cell-type specific morphological markers. Moreover, it permits: (1) semi-automated and reliable segmentation of all marker-positive cells within the volume, (2) automated extraction of geometrical and graph-based features for each reconstructed cell, or (3) filtering of artefactual structures.
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