Coronary Centerline Extraction Using Multiple Hypothesis Tracking and Minimal Paths
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1433
This paper describes an interactive approach to the identification of coronary arteries in 3D angiography images. The approach is based on a novel multiple hypothesis tracking methodology which is complemented with a standard minimal path search, and it allows for a complete segmentation with little manual labor. When evaluated using the 3D CT angiography data supplied with the MICCAI'08 workshop 3D Segmentation in the Clinic: A Grand Challenge II, 98% of the target coronary arteries could be segmented in about 5 minutes per data set with the same spatial accuracy achieved in manual segmentations by human experts.

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Categories: Data Representation, Derivatives and Integrals, Distance maps, Filtering, Higher order derivatives, Hypothesis Testing, Image, Mathematics, Optimization, PointSet, Resampling, Segmentation, Thresholding
Keywords: vessel, coronary, tracking, minimal paths, segmentation, multiple hypothesis tracking
Toolkits: ITK
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