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.
Uses a computational multiplexed image cytometry analysis toolbox to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. miCAT will be useful in all areas of tissue-based research.
Allows representation of multidimensional cellular measurements. PhenoPlot is a toolbox that permits visualization of up to 21 variables. It may be useful for determining the morphology of breast cancer cell lines or for understanding and interpreting multidimensional cellular imaging data. To assist users, this tool employs many visual elements such as differently sized, coloured and structured objects. It provides effective and intuitive pictorial representations of cellular phenotypes.
Provides general purpose functionality for reading, writing, processing and analysis of images. In the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. It uses ImageMagick to read and save images, and supports more than 80 image formats, including JPEG, TIFF, TGA, GIF and PNG. EBImage also supports standard geometric transformations such as rotation, reflection, cropping, translation and resizing. Classical image processing tools are available: linear filtering, morphological erosion and dilation, fast distance map computation, contour delineation and area filling.
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.
Allows users to develop image analysis algorithms. PhenoLOGIC is a machine learning algorithm based on Harmony software. It employs a learn-by-example approach to segment images and to identify phenotypes and cell populations. This software uses a linear classifier to enable classification of cells. PhenoLOGIC is an appropriate tool for the determination of cell cycle distribution in adherent cell cultures.
Offers access to a variety of advanced machine learning methods and provides an accurate high-content screen analysis. Advanced Cell Classifier (ACC) contains two features: (1) it is not limited to a specific classifier, (2) can detect even very small phenotype changes, allowing to identify not only the main but also subphenotypes. Its system permits to work with a graphical user interface (GUI) to create training database for classification, an immediate image classification, a plate browser, a report generation, and a numerous classification algorithms.
A fast and user-friendly software platform to segment cells, measure quantitative features of cellular phenotypes, construct discriminative profiles, and visualize the resulting cell masks and feature values. The cellXpress platform is designed to make fast and efficient high-throughput phenotypic profiling more accessible to the wider biological research community.
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.
Provides assistance for the analysis of high-throughput microscopy-based screens. imageHTS main features are segmentation of cells, extraction of quantitative cell features, prediction of cell types and visualization of data through web interface. This software offers a standardized access to remote screen data to facilitate the dissemination of high-throughput microscopy-based screens.
Provides an online solution for displaying and simulating the human cell cycle to explore the timeline of molecular events during cell cycle progression. CCB is a program able to visualize time series data representing various molecular activities over the course of a single cell cycle. It can be used for examining the temporal sequence of cell cycle events by aligning single-cell traces from time-lapse fluorescent microscopy experiments.
Combines state-of-the-art automated neuron tracing and machine learning-enabled neuron classification tools. Aivia provides methods for analyzing time-lapse images. It covers a wide range of applications such as cell/nuclei counting, cell/nuclei tracking, 3D neuron detection and analysis, machine learning cell classification, particle tracking, wound healing and calcium oscillation tracking. Aivia also comes with editing tools to help get even better results.
Allows multiple class classification, elucidating complex phenotypes. Enhanced CellClassifier is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.
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