Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes.
Provides all the tools you need to visualize, analyze and validate 3D fluorescence images from a wide range of confocal microscopy, widefield and high content screening systems and is fully integrated for a seamless user experience. Get a full picture of the biological process with rapid, interactive, high-resolution volume rendering of time resolved, multichannel 3D data sets using Volocity software.
A free, open-source system designed for flexible, high-throughput cell image analysis. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. The customizability of BIOCAT is expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation.
A computational framework to annotate complex cellular dynamics. A machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states was developed. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.
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).
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.
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.