matlab runtime software Search Results


matlab runtime 9.2  (MathWorks Inc)
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Matlab Runtime 9.2, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab runtime mcr  (MathWorks Inc)
90
MathWorks Inc matlab runtime mcr
Matlab Runtime Mcr, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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compiler runtime mcr 7.17 2012a  (MathWorks Inc)
90
MathWorks Inc compiler runtime mcr 7.17 2012a
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 <t>ctFIRE</t> <t>software.</t> (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
Compiler Runtime Mcr 7.17 2012a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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runtime version 8.3  (MathWorks Inc)
90
MathWorks Inc runtime version 8.3
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 <t>ctFIRE</t> <t>software.</t> (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
Runtime Version 8.3, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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runtime version 8.3 - by Bioz Stars, 2026-03
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runtime environment 2011b  (MathWorks Inc)
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MathWorks Inc runtime environment 2011b
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 <t>ctFIRE</t> <t>software.</t> (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
Runtime Environment 2011b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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runtime compiler (mcr)  (MathWorks Inc)
96
MathWorks Inc runtime compiler (mcr)
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 <t>ctFIRE</t> <t>software.</t> (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
Runtime Compiler (Mcr), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab runtime 9.9  (MathWorks Inc)
90
MathWorks Inc matlab runtime 9.9
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 <t>ctFIRE</t> <t>software.</t> (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
Matlab Runtime 9.9, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tscratch software version 1.0 for windows  (MathWorks Inc)
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MathWorks Inc tscratch software version 1.0 for windows
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 <t>ctFIRE</t> <t>software.</t> (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
Tscratch Software Version 1.0 For Windows, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab runtime software  (MathWorks Inc)
90
MathWorks Inc matlab runtime software
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 <t>ctFIRE</t> <t>software.</t> (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
Matlab Runtime Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab-based software  (MathWorks Inc)
90
MathWorks Inc matlab-based software
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 <t>ctFIRE</t> <t>software.</t> (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
Matlab Based Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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runtime mcr software  (MathWorks Inc)
90
MathWorks Inc runtime mcr software
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 <t>ctFIRE</t> <t>software.</t> (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
Runtime Mcr Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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compiler runtime (mcr 64-bit)  (MathWorks Inc)
90
MathWorks Inc compiler runtime (mcr 64-bit)
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 <t>ctFIRE</t> <t>software.</t> (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
Compiler Runtime (Mcr 64 Bit), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


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

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