The WiSoft Minerva-image analysis for developers is considered to be the ultimate solution for cell biology assay development, visualization, analysis, and statistical assessment of experiments.
It provides easy-to-use, flexible and modular tools enabling strong, yet adaptable image processing and analysis.
Image Credit: IDEA Bio-Medical Ltd.
Overview
WiSoft® Minerva analysis software package provides extremely adaptable image processing, segmentation, quantification, multi-parametric statistical analysis, and a range of image and data visualization tools.
The rich set of image analysis functions and modules jointly with the huge repertoire of visualization tools available for images and output data are adaptably collected into an analysis pipeline in a workflow-based interface.
Tailor-made analysis scripts
Highly flexible solutions have been offered by Minerva for collecting unbiased data obtained from samples of an extensive range of cellular immunological, biological, pharmaceutical, and biomedical applications. The software’s flexible, easy-to-use, and modular tools enable strong, yet adaptable image processing and analysis.
To be a step forward for users who are increasing research requirements, Minerva includes image analysis building blocks for making high-content image analysis applications that have been customized to suit ever-advancing research.
High resolution analysis
Without giving up speed, Minerva has been developed for examining large image data sets at high processing speeds. The advanced statistical evaluations and image processing algorithms of multi-parametric data allow the harvesting of rich information without sacrificing the analysis speed.
Straightforward visualization tools
A large range of visualization and data presentation tools are provided for the display of outcomes, like scatter plots, heat maps, histograms, plots, and graphs of numerical data, sub-population tools, and many more.
Easily create analysis scripts
Minerva allows biologists who are non-image analysis experts to design image analysis scripts. Images can be segmented with just a few clicks of the mouse and then tailored algorithms developed quickly and easily. Minerva’s modularity enables the adaptation of ready-made analysis modules for specific biological assays, simple programming of analysis scripts using pre-configured image analysis functions, and high-level programming, enabling the addition of algorithms for customized functions.
The workflow-based interface consists of simple navigation through the analysis modules for the simple creation of new functions. The software offers flexible input and output formats and a uniform data structure to allow the simple merging of functions from external sources.
Powerful multi-parametric statistical evaluation tools
Minerva performs multi-parametric analysis on the impacts of treated cells that have been specified by multi-color, morphological, and time-dependent features and compared to controls with the help of a range of statistical scores.
A statistical program menu provides quick improved image pre-processing algorithms, which is a rich choice of segmentation techniques for determining cells and intracellular features and quantification of intensity, morphological, and texture features. Strong statistical tools allow definition of effects and reactions to perturbations in multi-parametric space, as well as combining screening data for interactive data mining and global visualization.
A unique Outliers tool enables the exception of objects that display values that markedly tend to deviate from other objects in the data set out of the statistical calculations to obtain a highly precise and dependable presentation of the outcomes.
Algorithm library
Partial list of analysis applications
- Cell count (such as separation of cells in aggregates)
- Cell death (mitotic, toxicity)
- Cell shape and polarity are available
- Cell tracking (migration, time-dependence of features)
- Comes with nuclear and sub-nuclear structures
- Microtubule cytoskeleton features
- Actin cytoskeleton features
- Endoplasmic reticulum features
- Focal adhesion features
- Cytoplasm-to-nucleus ratio
- Intracellular vesicle features
- Golgi organization
- Mitochondria features
Image processing modules (partial list):
- Sub-cellular co-localization and image correlation
- Microtubule dynamics assay
- Proteasome inhibition quantification
- Bacteriology (counting and sorting bacteria)
- Immunology
- Multi-color analysis of lymphoid cells
Pre-processing: Field correction, background estimation, de-noising, and subtraction.
Segmentation: Texture enhancement thresholding methods, contrast enhancement, connected components “blobs” (WatersShed, binary, SeededWaterShed), points, fibers, mask merging, splitting, and aggregation of segments, and secondary masks.
Quantification: Morphological attributes: perimeter, area, convex hull, best-fitted ellipsoid, solidity, eccentricity, polarity, long-to-short axis ratio.
Fluorescent intensity attributes: Average intensity, total intensity, color intensity ratios, background-subtracted intensities, and Intensity-weighted textural.
Dynamic attributes: Cell-by-cell time dependence of attributes, moving speed, drug-response curves, and intracellular transport speeds
Statistics: Single-parameter mean, percentiles, median, and non-parametric distribution comparisons, multi-parametric methods (for example, extended principal components, clustering, and Mahalanobis-based scores)
Image processing tools
Pre-processing: De-noising, field correction, background estimation, and subtraction
Segmentation: Contrast enhancement, connected components “blobs”, fibers, points, mask merging, splitting and aggregation of segments and secondary masks, texture enhancement thresholding methods
Quantification morphological attributes: Area, perimeter, convex hull, best-fitted ellipsoid, polarity, solidity, eccentricity, long-to-short axis ratio.
Fluorescent intensity attributes: Total intensity, background-subtracted intensities, color intensity ratios, average intensity, and intensity-weighted textural.
Dynamic attributes: cell-by-cell time dependence of attributes, intracellular transport speeds, moving speed, and drug-response curves
Statistics: Single-parameter mean, percentiles, median, and non-parametric distribution comparisons, multi-parametric methods (for example, extended principal components, clustering, and Mahalanobis-based scores)
Biomarker imaging: Quantitative analysis of cellular biomarkers
Cell morphology and Cell-level quantification: Precise cell edge detection and segmentation at sparse culture and in cell colonies.
Sub-cellular features; detection and quantification: Nuclei RNA (mRNA, RNAi) DNA (mtDNA) Actin Cytoskeleton Microtubules Golgi Vesicles Focal Adhesions Mitochondria.
Dynamic processes; tracking events along with high temporal resolution: Cell Dynamics Microtubule movement, growth, and shrinkage; Protein expression and localization; Wound healing scratch assay; Phago-kinetic tracks
Model systems: Animal cells, fixed cells, live cells, primary cells, viruses, bacteria, yeast, spheroids, Zebrafish, organoids, and C-elegans.
Specifications
Operation system
Microsoft Windows® XP
Modularity
- Script-based, pre-configured templates available for a rapid learning curve and quick customization during assay development, scalable deployment
- User-friendly modules for a large range of applications
- Manual parameter tuning tool
- Aids custom development of new functions and algorithms
- Functions available for user-built analysis sequences
Import/export
- Adaptable input image format
- Import of general microscope image formats
- Exports images and masks to JPEG, TIFF, and BMP format, and numerical data available in CSV format
Speed
Quick processing of complicated data sets enables the analysis of images at the time of acquisition.
Visualization
- Single image
- Overlays of multicolor images and masks
- Montage or tile of multiple images
- Plots and graphs of numerical data
Image analysis and statistical tools:
- Segmentation techniques for cells and intracellular features
- Automatic exclusion of outliers
- Non-parametric statistics tools
- Measurement of morphological and fluorescence intensity and texture features
- Multi-parametric analysis tools
- Interactive data mining