Soft Plaque Detection and Automatic Vessel Segmentation
Author(s)
Lankton, Shawn
Stillman, Arthur
Raggi, Paolo
Tannenbaum, Allen R.
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Abstract
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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Date
2009-09-20
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Proceedings