compiled matlab app (windows only, no matlab license required)  (MathWorks Inc)

Journal: Methods in molecular biology (Clifton, N.J.)

Article Title: Intravital Imaging of Tumor Cell Motility in the Tumor Microenvironment Context

doi: 10.1007/978-1-4939-7701-7_14

Figure Lengend Snippet: Intravital Systems Microscopy: Multiparametric SVM classification of tumor cell locomotion and microenvironmental parameters (a) Identifying and quantifying fast-locomoting cells. Left panels, raw images of an individual z-section from the 4D stack (at 0 and 60 min; 0′ and 60′). Middle panels, 0′ was subtracted from 60′, resulting in Δ60 (top); image was then thresholded/binarized (bottom). Right panels, results of motility analysis including quantification of fast locomoting cells (top) and the overlay with the 0′ image (bottom). Scale bar 50 μm. (b) Identifying and quantifying invadopodia in slow locomoting cells. Raw images of a cell at time 0, with fully extended invadopodium at 3 min and partially retracted invadopodium at 15 min. Overlays Δ3 and Δ15 show invadopodia extension highlighted in magenta. Scale bar 10 μm. (c) Binarized images used for extraction of microenvironmental parameters. Image in Fig. 1c was separated and thresholded, resulting in binary images of collagen (magenta), tumor cells (green), macrophages (cyan) and blood vessels (red). (c′) Collagen fiber map (left) and dimensionless straightness histogram (right) from ctFIRE software. (d) 3D projection of SVM classification results. Red spheres denote slow locomotion, blue-fast locomotion, green- misclassifications. The size of the spheres indicates the number of locomoting cells in the FOV. Dmax (μm) stands for the diameter of the largest blood vessel in the FOV, macrophages (%) and collagen (%) stand for the thresholded area in respective channels

Article Snippet: The ctFIRE software requires MATLAB compiler runtime (MCR 7.17 2012a) installation.

Techniques: Microscopy, Extraction, Software

Journal: Journal of nuclear medicine : official publication, Society of Nuclear Medicine

Article Title: Integrated Software Environment Based on COMKAT for Analyzing Tracer Pharmacokinetics with Molecular Imaging

doi: 10.2967/jnumed.109.064824

Figure Lengend Snippet: Summary and Comparison of Functionalities of COMKAT Distributions

Article Snippet: Behind scenes, COMKAT GUI calls COMKAT command-line functions to calculate model output and estimate parameters. table ft1 table-wrap mode="anchored" t5 TABLE 1 caption a7 Function COMKAT on MATLAB Compiled COMKAT application COMKAT GUI Loading of input functions from files Yes Yes Simulation of model output Yes Yes Creation of new kinetic models Yes No Parameter estimation Yes Yes Loading of tissue time–activity curves from files Yes Yes Loading of tissue time–activity curves from COMKAT image tool Yes Yes Calculation of parametric images Yes Yes Distributed computing for parametric imaging * Yes No COMKAT image tool Support for multiple image formatsYes Yes Image display and contrast adjustments Yes Yes Frame summation Yes Yes Spatial filtering Yes Yes Drawing of ROIs or volumes of interest Yes Yes Image coregistration Yes Yes Image translation and rotation Yes Yes Image reslicing in arbitrary orientations Yes Yes MATLAB scripting with COMKAT command-line functions Yes No Available for Windows, Linux, and MacOS X † Yes Yes COMKAT licensing Free for academic research use Free for academic research use MATLAB licensing Requires MATLAB installation and licenses Requires MATLAB Compiler Runtime (no licensing fees) Open in a separate window * Requires MATLAB licenses for MATLAB Distributed Computing Server and Parallel Computing Toolbox.

Techniques: Comparison, Imaging