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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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Schematic for the determination of vessel radius algorithm.A. The starting or current position (0,0) is shown by the red “X”. First, the sampling moves three pixels to the right (3,0). If a value of 105 is not found, the algorithm begins its counter-clockwise search for a pixel of value 105. B. Counter-clockwise movement is initiated by moving to pixel (3,1) for sampling. C. Counter-clockwise movements until a 105-valued pixel is found at position (−1,2). D. Walking in an orthogonally prescribed direction is completed until a black (0-valued) pixel is found. The green arrows represent the first “unsuccessful” walk and blue arrows represent the second “successful” walk. Once a black pixel is found, walking ceases and radius is calculated from the hypotenuse (h) of Pythagorean Theorem, where “a” and “b” represent the horizontal and vertical walking distance, respectively.
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pone-0020807-g005: Schematic for the determination of vessel radius algorithm.A. The starting or current position (0,0) is shown by the red “X”. First, the sampling moves three pixels to the right (3,0). If a value of 105 is not found, the algorithm begins its counter-clockwise search for a pixel of value 105. B. Counter-clockwise movement is initiated by moving to pixel (3,1) for sampling. C. Counter-clockwise movements until a 105-valued pixel is found at position (−1,2). D. Walking in an orthogonally prescribed direction is completed until a black (0-valued) pixel is found. The green arrows represent the first “unsuccessful” walk and blue arrows represent the second “successful” walk. Once a black pixel is found, walking ceases and radius is calculated from the hypotenuse (h) of Pythagorean Theorem, where “a” and “b” represent the horizontal and vertical walking distance, respectively.

Mentions: To calculate the radius, the smoothed binary and skeletonized images were used to create a composite image. A composite image is obtained as a means to distinguish the center line (gray), and the vessel (white) from the background of the image (black). From this, one can determine vessel trajectory and the edge of the vessel. First, the skeleonized white (255) pixels were changed to pixels with values of 150. The 150-value skeletonized image was subtracted from the smoothed binary image, creating a composite image with three color values, black (0), grey (105) and white (255). The resulting composite image is shown in Figure 5A.


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

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

Schematic for the determination of vessel radius algorithm.A. The starting or current position (0,0) is shown by the red “X”. First, the sampling moves three pixels to the right (3,0). If a value of 105 is not found, the algorithm begins its counter-clockwise search for a pixel of value 105. B. Counter-clockwise movement is initiated by moving to pixel (3,1) for sampling. C. Counter-clockwise movements until a 105-valued pixel is found at position (−1,2). D. Walking in an orthogonally prescribed direction is completed until a black (0-valued) pixel is found. The green arrows represent the first “unsuccessful” walk and blue arrows represent the second “successful” walk. Once a black pixel is found, walking ceases and radius is calculated from the hypotenuse (h) of Pythagorean Theorem, where “a” and “b” represent the horizontal and vertical walking distance, respectively.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3111429&req=5

pone-0020807-g005: Schematic for the determination of vessel radius algorithm.A. The starting or current position (0,0) is shown by the red “X”. First, the sampling moves three pixels to the right (3,0). If a value of 105 is not found, the algorithm begins its counter-clockwise search for a pixel of value 105. B. Counter-clockwise movement is initiated by moving to pixel (3,1) for sampling. C. Counter-clockwise movements until a 105-valued pixel is found at position (−1,2). D. Walking in an orthogonally prescribed direction is completed until a black (0-valued) pixel is found. The green arrows represent the first “unsuccessful” walk and blue arrows represent the second “successful” walk. Once a black pixel is found, walking ceases and radius is calculated from the hypotenuse (h) of Pythagorean Theorem, where “a” and “b” represent the horizontal and vertical walking distance, respectively.
Mentions: To calculate the radius, the smoothed binary and skeletonized images were used to create a composite image. A composite image is obtained as a means to distinguish the center line (gray), and the vessel (white) from the background of the image (black). From this, one can determine vessel trajectory and the edge of the vessel. First, the skeleonized white (255) pixels were changed to pixels with values of 150. The 150-value skeletonized image was subtracted from the smoothed binary image, creating a composite image with three color values, black (0), grey (105) and white (255). The resulting composite image is shown in Figure 5A.

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