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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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Related in: MedlinePlus

Overview of RAVE GUI.A. A representative screenshot of RAVE . The user is able to input parameters in the editable fields on the left and monitor the binarization and skeltonization of the image before ending the fitting process and recording data. The “Binary Image” is shown after being binarized, smoothed using a Gaussian filter, then binarized again. B. In vivo image of tumor associated vasculature can be analyzed by RAVE. C. Ex vivo whole mounted pancreas vasculature was analyzed by RAVE. D. Ex vivo whole mounted spinotrapezius images was analyzed.
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pone-0020807-g001: Overview of RAVE GUI.A. A representative screenshot of RAVE . The user is able to input parameters in the editable fields on the left and monitor the binarization and skeltonization of the image before ending the fitting process and recording data. The “Binary Image” is shown after being binarized, smoothed using a Gaussian filter, then binarized again. B. In vivo image of tumor associated vasculature can be analyzed by RAVE. C. Ex vivo whole mounted pancreas vasculature was analyzed by RAVE. D. Ex vivo whole mounted spinotrapezius images was analyzed.

Mentions: Because MATLAB is a common engineering and science computing language accessible to most investigators, an open-source vessel analysis tool designed in MATLAB can be used and/or customized easily by all who are interested. As is, our software Rapid Analysis of Vessel Elements (RAVE), shown in Figure 1, quickly and accurately analyzes crucial vessel elements in understanding physiologic and pathologic angiogenesis in second to minutes. Using pancreatic tumor vasculature as a relevant test-bed for validating our new tool, RAVE rapidly detects a significant increase in vessel volume fraction (VVF), vessel length density (VLD), vessel radius and fractal dimension of pancreatic tumor vasculature compared to normal pancreatic vasculature.


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

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

Overview of RAVE GUI.A. A representative screenshot of RAVE . The user is able to input parameters in the editable fields on the left and monitor the binarization and skeltonization of the image before ending the fitting process and recording data. The “Binary Image” is shown after being binarized, smoothed using a Gaussian filter, then binarized again. B. In vivo image of tumor associated vasculature can be analyzed by RAVE. C. Ex vivo whole mounted pancreas vasculature was analyzed by RAVE. D. Ex vivo whole mounted spinotrapezius images was analyzed.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3111429&req=5

pone-0020807-g001: Overview of RAVE GUI.A. A representative screenshot of RAVE . The user is able to input parameters in the editable fields on the left and monitor the binarization and skeltonization of the image before ending the fitting process and recording data. The “Binary Image” is shown after being binarized, smoothed using a Gaussian filter, then binarized again. B. In vivo image of tumor associated vasculature can be analyzed by RAVE. C. Ex vivo whole mounted pancreas vasculature was analyzed by RAVE. D. Ex vivo whole mounted spinotrapezius images was analyzed.
Mentions: Because MATLAB is a common engineering and science computing language accessible to most investigators, an open-source vessel analysis tool designed in MATLAB can be used and/or customized easily by all who are interested. As is, our software Rapid Analysis of Vessel Elements (RAVE), shown in Figure 1, quickly and accurately analyzes crucial vessel elements in understanding physiologic and pathologic angiogenesis in second to minutes. Using pancreatic tumor vasculature as a relevant test-bed for validating our new tool, RAVE rapidly detects a significant increase in vessel volume fraction (VVF), vessel length density (VLD), vessel radius and fractal dimension of pancreatic tumor vasculature compared to normal pancreatic vasculature.

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