Actualizado 16 Oct 2020
BiofilmQ is an advanced biofilm analysis tool for quantifying the properties of cells inside large 3-dimensional biofilm communities in space and time. It can work with many different kinds of 3D biofilm images, including flow-chamber biofilms, colonies on agar, pellicles, and aggregates.
BiofilmQ is based on a graphical user interface and does not require programming expertise or prior knowledge of image analysis.
BiofilmQ has a unique approach to quantifying biofilm properties in space and time: BiofilmQ does not require the optical resolution of single cells. Instead, BiofilmQ uses a different segmentation approach, based on dividing the biofilm biovolume obtained by threshold-based 3D segmentation into cubic pseudo-cells. Each cube is treated as a single cell, for which fluorescence, architectural, spatial, and many more properties are measured. By using this cube-based approach, it is possible to perform biofilm image cytometry (analogious to flow cytometry, but with spatial features), based on the quantification of many parameters for pseudo-cells.
BiofilmQ provides a powerful analysis and plotting functionality for presenting the data from each pseudo-cell cube. It is possible, e.g. to generate biofilm kymographs, demographs, and to generate flow-cytometry-like datasets, including population gating, which include not just fluorescence and structural properties of cells, but also spatial properties within the biofilm.
The easy-to-use analysis and visualization features enable you to generate high-quality data figures without any programming skills.
For advanced users with programming expertise, all features are fully scriptable (in MATLAB) for batch processing of large datasets.
Hartmann, Raimo, et al. BiofilmQ, a Software Tool for Quantitative Image Analysis of Microbial Biofilm Communities. Cold Spring Harbor Laboratory, Aug. 2019, doi:10.1101/735423.
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformasWindows macOS Linux
Inspirado por: GUI Layout Toolbox, SplashScreen, Efficient subpixel image registration by cross-correlation, isToolboxAvailable, Neighbour points in a matrix, Progress bar for matlab loops (incl. parfor), parfor_progressbar, findjobj - find java handles of Matlab graphic objects, UICOMPONENT - expands uicontrol to all Java classes, cooc3d, writeFCS(fname, DATA, TEXT, OTHER), folderSizeTree, Skeleton3D, FFT-based convolution, Tree data structure as a MATLAB class, fit_ellipse, image ellipsoid 3D, 2D Histogram Calculation, Cell Array to CSV-file [cell2csv.m], Ridler-Calvard image thresholding, Marching Cubes, Disk usage, stlwrite - write ASCII or Binary STL files, Noise Level Estimation from a Single Image, imshow3D, rgb2hex and hex2rgb, Text progress bar, ordfilt3, 2D Autocorrelation function, Minimal Bounding Box, sort_nat: Natural Order Sort
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