Bayesian Maximal Paths for Coronary Artery Segmentation from 3D CT Angiograms
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Design and study of flux-based features for 3D vascular tracking
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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In this paper, we propose a Minimum Average-cost Path (MACP) model for segmenting 3D coronary arteries by minimizing the average edge cost along path in discrete 4D graph constructed by image voxels and associated radii. Prim's Minimum Spanning Tree method is used for efficient optimization of the MACP model. The centerline and the radii of the cross sections of the coronary artery are extracted simultaneously during the optimization. The method does not need any image preprocessing steps and has been intensively validated as an effective approach with the Rotterdam Coronary Artery Algorithm Evaluation Framework [1]. The computational cost of the proposed method is particularly low (7.467 seconds per segment, 18.5mm/s on average), which makes real time segmentation of coronary artery possible. Shortcut problem, which is a classic issue of the minimal path techniques, can also be overcome by the proposed method.