Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
A Bayesian Approach for Liver Analysis: Algorithm and Validation Study
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images
Image and Vision Computing
Bayesian tracking of tubular structures and its application to carotid arteries in CTA
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Accurate banded graph cut segmentation of thin structures using laplacian pyramids
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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We present a nearly automatic graph-based segmentation method for patient specific modeling of the aortic arch and carotid arteries from CTA scans for interventional radiology simulation. The method starts with morphological-based segmentation of the aorta and the construction of a prior intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weights function that adaptively couples the voxel intensity, the intensity prior, and geometric vesselness shape prior. Finally, the same graph-cut optimization framework is used for nearly automatic removal of a few vessel segments and to fill minor vessel discontinuities due to highly significant imaging artifacts. Our method accurately segments the aortic arch, the left and right subclavian arteries, and the common, internal, and external carotids and their secondary vessels. It does not require any user initialization, parameters adjustments, and is relatively fast (150–470 secs). Comparative experimental results on 30 carotid arteries from 15 CTAs from two medical centres manually segmented by expert radiologist yield a mean symmetric surface distance of 0.79mm (std=0.25mm). The nearly automatic refinement requires about 10 seed points and took less than 2mins of treating physician interaction with no technical support for each case.