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Rapid analysis of vessel elements (RAVE): a tool for studying physiologic, pathologic and tumor angiogenesis.

Seaman ME, Peirce SM, Kelly K - PLoS ONE (2011)

Bottom Line: To this end, we have produced an automatic and rapid vessel detection and quantification system using a MATLAB graphical user interface (GUI) that vastly reduces time spent on analysis and greatly increases repeatability.In stark comparison, using our GUI, image analysis time is reduced to around 1 minute.This drastic reduction in analysis time coupled with increased repeatability makes this tool valuable for all vessel research especially those requiring rapid and reproducible results, such as anti-angiogenic drug screening.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.

ABSTRACT
Quantification of microvascular network structure is important in a myriad of emerging research fields including microvessel remodeling in response to ischemia and drug therapy, tumor angiogenesis, and retinopathy. To mitigate analyst-specific variation in measurements and to ensure that measurements represent actual changes in vessel network structure and morphology, a reliable and automatic tool for quantifying microvascular network architecture is needed. Moreover, an analysis tool capable of acquiring and processing large data sets will facilitate advanced computational analysis and simulation of microvascular growth and remodeling processes and enable more high throughput discovery. To this end, we have produced an automatic and rapid vessel detection and quantification system using a MATLAB graphical user interface (GUI) that vastly reduces time spent on analysis and greatly increases repeatability. Analysis yields numerical measures of vessel volume fraction, vessel length density, fractal dimension (a measure of tortuosity), and radii of murine vascular networks. Because our GUI is open sourced to all, it can be easily modified to measure parameters such as percent coverage of non-endothelial cells, number of loops in a vascular bed, amount of perfusion and two-dimensional branch angle. Importantly, the GUI is compatible with standard fluorescent staining and imaging protocols, but also has utility analyzing brightfield vascular images, obtained, for example, in dorsal skinfold chambers. A manually measured image can be typically completed in 20 minutes to 1 hour. In stark comparison, using our GUI, image analysis time is reduced to around 1 minute. This drastic reduction in analysis time coupled with increased repeatability makes this tool valuable for all vessel research especially those requiring rapid and reproducible results, such as anti-angiogenic drug screening.

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Validation of VVF and VLD.Ex vivo (A,B) and in vivo (C,D) comparison of RAVE and manually calculated VVF (A, C) and VLD (B, D). In each subpanel, the left and right plots present correlation and Bland-Altman analysis, respectively. Data is shown for multiple image fields in a single animal for both ex vivo and in vivo analyses.
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pone-0020807-g002: Validation of VVF and VLD.Ex vivo (A,B) and in vivo (C,D) comparison of RAVE and manually calculated VVF (A, C) and VLD (B, D). In each subpanel, the left and right plots present correlation and Bland-Altman analysis, respectively. Data is shown for multiple image fields in a single animal for both ex vivo and in vivo analyses.

Mentions: Validation of manual and RAVE calculated Vessel Volume Fraction and Vessel Length Density was accomplished by plotting manually and RAVE measured VVF and VLD against each other, then calculating a correlation coefficient (Figure 2, insets). In both VVF and VLD, in vivo images had higher correlation coefficients (0.996 and 0.987, respectively) indicating that RAVE more accurately calculated VVF and VLD in intravital images of tumor microvasculature. Although slightly less, the correlation coefficient of VVF and VLD in ex vivo images of the microvasculature contained within mouse spinotrapezius muscle (0.986 and 0.979) was well above 0.95, lending confidence in the accuracy of RAVE calculated VVF and VLD compared to manual methods, both in vivo and ex vivo.


Rapid analysis of vessel elements (RAVE): a tool for studying physiologic, pathologic and tumor angiogenesis.

Seaman ME, Peirce SM, Kelly K - PLoS ONE (2011)

Validation of VVF and VLD.Ex vivo (A,B) and in vivo (C,D) comparison of RAVE and manually calculated VVF (A, C) and VLD (B, D). In each subpanel, the left and right plots present correlation and Bland-Altman analysis, respectively. Data is shown for multiple image fields in a single animal for both ex vivo and in vivo analyses.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111429&req=5

pone-0020807-g002: Validation of VVF and VLD.Ex vivo (A,B) and in vivo (C,D) comparison of RAVE and manually calculated VVF (A, C) and VLD (B, D). In each subpanel, the left and right plots present correlation and Bland-Altman analysis, respectively. Data is shown for multiple image fields in a single animal for both ex vivo and in vivo analyses.
Mentions: Validation of manual and RAVE calculated Vessel Volume Fraction and Vessel Length Density was accomplished by plotting manually and RAVE measured VVF and VLD against each other, then calculating a correlation coefficient (Figure 2, insets). In both VVF and VLD, in vivo images had higher correlation coefficients (0.996 and 0.987, respectively) indicating that RAVE more accurately calculated VVF and VLD in intravital images of tumor microvasculature. Although slightly less, the correlation coefficient of VVF and VLD in ex vivo images of the microvasculature contained within mouse spinotrapezius muscle (0.986 and 0.979) was well above 0.95, lending confidence in the accuracy of RAVE calculated VVF and VLD compared to manual methods, both in vivo and ex vivo.

Bottom Line: To this end, we have produced an automatic and rapid vessel detection and quantification system using a MATLAB graphical user interface (GUI) that vastly reduces time spent on analysis and greatly increases repeatability.In stark comparison, using our GUI, image analysis time is reduced to around 1 minute.This drastic reduction in analysis time coupled with increased repeatability makes this tool valuable for all vessel research especially those requiring rapid and reproducible results, such as anti-angiogenic drug screening.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.

ABSTRACT
Quantification of microvascular network structure is important in a myriad of emerging research fields including microvessel remodeling in response to ischemia and drug therapy, tumor angiogenesis, and retinopathy. To mitigate analyst-specific variation in measurements and to ensure that measurements represent actual changes in vessel network structure and morphology, a reliable and automatic tool for quantifying microvascular network architecture is needed. Moreover, an analysis tool capable of acquiring and processing large data sets will facilitate advanced computational analysis and simulation of microvascular growth and remodeling processes and enable more high throughput discovery. To this end, we have produced an automatic and rapid vessel detection and quantification system using a MATLAB graphical user interface (GUI) that vastly reduces time spent on analysis and greatly increases repeatability. Analysis yields numerical measures of vessel volume fraction, vessel length density, fractal dimension (a measure of tortuosity), and radii of murine vascular networks. Because our GUI is open sourced to all, it can be easily modified to measure parameters such as percent coverage of non-endothelial cells, number of loops in a vascular bed, amount of perfusion and two-dimensional branch angle. Importantly, the GUI is compatible with standard fluorescent staining and imaging protocols, but also has utility analyzing brightfield vascular images, obtained, for example, in dorsal skinfold chambers. A manually measured image can be typically completed in 20 minutes to 1 hour. In stark comparison, using our GUI, image analysis time is reduced to around 1 minute. This drastic reduction in analysis time coupled with increased repeatability makes this tool valuable for all vessel research especially those requiring rapid and reproducible results, such as anti-angiogenic drug screening.

Show MeSH
Related in: MedlinePlus