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.
Allows to implement NBLAST neuron similarity algorithm. Nat.nblast is an extension of the “nat” software, and supplies a collection of NBLAST-related functions. It executes search of databases for neurons, and permits to compare groups of neurons. A distance matrix for hierarchical clustering can be produced by this tool.
Allows users to detect and count fluorescent signals in microscopy images of cells. Blob Finder is a free software that performs two types of analysis: (i) an average count, for quantifying the number of nuclei and signals in an image; (ii) and a single cell analysis, that assigns each signal to the closest cell and get a signal count for each cell in the image. It also performs on-z_stacks of the cell with a maximum projection to project the image data into a 2D image.
Provides a high-throughput image processing workflow designed for reducing hands-on analysis time confocal, epi-fluorescence, and two-photon microscopy images. Chrysalis can automate image processing steps. This method identifies subtle differences in cell phenotype and cell-cell interactions, while also offering significant reduction in hands-on analysis time. It can be applied to a broad range of biological questions.
Assists in digital phantoms and simulation of image formation in 3d cell imaging. CytoPacq is a toolbox that consists of three individual modules: (i) 3D-cytogen, a module generating the digital cell phantoms, (ii) 3D-optigen, a module simulating the transmission of the signal through the optical system and (iii) 3D-acquigen, a module simulating the phenomenon manifesting themselves during image capture with digital CCD cameras.
Facilitates the application of deep learning in cytometry, especially for analyzing imaging flow cytometry (IFC) data. Deepometry uses a Keras-based implementation of the ResNet50 neural network, alongside other logistic modules. The software was applied to characterize morphological profiles of red blood cells (RBCs) at different time points.
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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