Difference between revisions of "Projects:SoftPlaqueDetection"
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The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This is a particularly challenging problem in computed tomography angiography (CTA) imagery because plaques may have similar appearance to nearby blood and muscle tissue. This paper presents an effective technique for automatically detecting soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. The proposed method identifies plaques that exist within the vessel wall by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies that allow the complex appearances of plaques and vessels to be modeled with simple statistics. This method is shown to be an effective way to detect the minute variations that distinguish plaques from healthy tissue. Experiments demonstrating the effectiveness of the algorithm are performed on eight datasets, and results are compared with detections provided by an expert cardiologist. | The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This is a particularly challenging problem in computed tomography angiography (CTA) imagery because plaques may have similar appearance to nearby blood and muscle tissue. This paper presents an effective technique for automatically detecting soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. The proposed method identifies plaques that exist within the vessel wall by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies that allow the complex appearances of plaques and vessels to be modeled with simple statistics. This method is shown to be an effective way to detect the minute variations that distinguish plaques from healthy tissue. Experiments demonstrating the effectiveness of the algorithm are performed on eight datasets, and results are compared with detections provided by an expert cardiologist. | ||
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This technique allows region-based segmentation energies to be spatially localized such that statistical models of the foreground and background adapt to image information as it changes over the domain of the image. This allows for improved modeling accuracy with simplified statistical models. Furthermore, it is particularly powerful for segmenting vessels, which often exhibit changing image intensities over their length, and for the identification of non-calcified plaques, which typically have only slight intensity differences from surrounding structures. | This technique allows region-based segmentation energies to be spatially localized such that statistical models of the foreground and background adapt to image information as it changes over the domain of the image. This allows for improved modeling accuracy with simplified statistical models. Furthermore, it is particularly powerful for segmenting vessels, which often exhibit changing image intensities over their length, and for the identification of non-calcified plaques, which typically have only slight intensity differences from surrounding structures. | ||
Revision as of 18:15, 20 October 2009
Home < Projects:SoftPlaqueDetectionBack to Georgia Tech Algorithms
Soft Plaque Detection
Description
The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This is a particularly challenging problem in computed tomography angiography (CTA) imagery because plaques may have similar appearance to nearby blood and muscle tissue. This paper presents an effective technique for automatically detecting soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. The proposed method identifies plaques that exist within the vessel wall by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies that allow the complex appearances of plaques and vessels to be modeled with simple statistics. This method is shown to be an effective way to detect the minute variations that distinguish plaques from healthy tissue. Experiments demonstrating the effectiveness of the algorithm are performed on eight datasets, and results are compared with detections provided by an expert cardiologist.
This technique allows region-based segmentation energies to be spatially localized such that statistical models of the foreground and background adapt to image information as it changes over the domain of the image. This allows for improved modeling accuracy with simplified statistical models. Furthermore, it is particularly powerful for segmenting vessels, which often exhibit changing image intensities over their length, and for the identification of non-calcified plaques, which typically have only slight intensity differences from surrounding structures.
First, vessels are segmented using a localized energy that is ideal for vessel segmentation, because it allows the surface to expand into areas of similar local intensity as long as a larger difference exists between local interiors and exteriors. This allows rapid segmentation of vessels despite changing intensities along the length of the vessel.
Next, two initial surfaces are created that lie just inside and just outside the initial segmented surface. A separate localized energy is used to move the two surfaces toward each other. This energy is chosen to emphasize local differences so that expansion into nearby regions that have slightly different intensities is discouraged, even if the local means are similar. This more stringent constraint is quite valuable when attempting to differentiate between vascular plaques and surrounding tissue.
Areas where the two surfaces do not find the same boundary are identified as plaques.
Ongoing Work
Future work on this method will include coupling the evolution of the interior and exterior surfaces so that information about local intensities and geometries can be shared in order to detect plaques more robustly. Furthermore, a larger study is planned in which a larger number of datasets will be analyzed, a quantitative analysis will be performed, and the method will be compared with intravascular ultrasound imagery to confirm the presence and composition of detected plaques. We believe this work has the potential of being an important step forward in automatically detecting non-calcified plaques, which have been clearly linked with the occurrence of heart attacks and stroke.
Key Investigators
Georgia Tech: Shawn Lankton, Jacob Huang, Vandana Mohan, and Allen Tannenbaum
Publications
In Print
