Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach.

PURPOSE The application and large-scale evaluation of minimum cost path approaches for coronary centerline extraction from computed tomography coronary angiography (CTCA) data and the development and evaluation of a novel method to reduce the user-interaction time. METHODS A semiautomatic method based on a minimum cost path approach is evaluated for two different cost functions. The first cost function is based on a frequently used vesselness measure and intensity information, and the second is a recently proposed cost function based on region statistics. User interaction is minimized to one or two mouse clicks distally in the coronary artery. The starting point for the minimum cost path search is automatically determined using a newly developed method that finds a point in the center of the aorta in one of the axial slices. This step ensures that all computationally expensive parts of the algorithm can be precomputed. RESULTS The performance of the aorta localization procedure was demonstrated by a success rate of 100% in 75 images. The success rate and accuracy of centerline extraction was quantitatively evaluated on 48 coronary arteries in 12 images by comparing extracted centerlines with a manually annotated reference standard. The method was able to extract 88% and 47% of the vessel center-lines correctly using the vesselness/intensity and region statistics cost function, respectively. For only the proximal part of the vessels these values were 97% and 86%, respectively. Accuracy of centerline extraction, defined as the average distance from correctly automatically extracted parts of the centerline to the reference standard, was 0.64 mm for the vesselness/intensity and 0.51 mm for the region statistics cost function. The interobserver variability was 99% for the success rate measure and 0.42 mm for the accuracy measure. Qualitative evaluation using the best performing cost function resulted in successful centerline extraction for 233 out of the 252 coronaries (92%) in 63 additional CTCA images. CONCLUSIONS The presented results, in combination with minimal user interaction and low computation time, show that minimum cost path approaches can effectively be applied as a preprocessing step for subsequent analysis in clinical practice and biomedical research.

[1]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[3]  Alejandro F. Frangi,et al.  3D MRA coronary axis determination using a minimum cost path approach , 2002, Magnetic resonance in medicine.

[4]  Wiro J Niessen,et al.  Imaging of small high-density structures in CT A phantom study. , 2006, Academic radiology.

[5]  Max A. Viergever,et al.  Fast delineation and visualization of vessels in 3-D angiographic images , 2000, IEEE Transactions on Medical Imaging.

[6]  Max W. K. Law,et al.  Efficient Implementation for Spherical Flux Computation and Its Application to Vascular Segmentation , 2009, IEEE Transactions on Image Processing.

[7]  Philippe C. Cattin,et al.  Automatic Ascending Aorta Detection in CTA Datasets , 2008, Bildverarbeitung für die Medizin.

[8]  Isabelle Bloch,et al.  Medial-based Bayesian tracking for vascular segmentation: Application to coronary arteries in 3D CT angiography , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  Laurent D. Cohen,et al.  Fast extraction of minimal paths in 3D images and applications to virtual endoscopy , 2001, Medical Image Anal..

[10]  Stefan Wesarg,et al.  Segmentation of vessels: the corkscrew algorithm , 2004, SPIE Medical Imaging.

[11]  Theo van Walsum,et al.  SEMI-AUTOMATIC CORONARY ARTERY CENTERLINE EXTRACTION IN COMPUTED TOMOGRAPHY ANGIOGRAPHY DATA , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[12]  Max A. Viergever,et al.  Minimum cost path determination using a simple heuristic function , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  Albert C. S. Chung,et al.  Probabilistic vessel axis tracing and its application to vessel segmentation with stream surfaces and minimum cost paths , 2007, Medical Image Anal..

[14]  Takashi Hanakawa,et al.  Blood vessel segmentation for head MRA using branch-based region growing , 2005 .

[15]  T. Boskamp,et al.  New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. , 2004, Radiographics : a review publication of the Radiological Society of North America, Inc.

[16]  Max A. Viergever,et al.  Vessel Axis Determination Using Wave Front Propagation Analysis , 2001, MICCAI.

[17]  Max A. Viergever,et al.  Multiscale vessel tracking , 2004, IEEE Transactions on Medical Imaging.

[18]  Gary S. Mintz,et al.  The site of plaque rupture in native coronary arteries: a three-vessel intravascular ultrasound analysis. , 2005, Journal of the American College of Cardiology.

[19]  Dirk Bartz,et al.  Scaffolding-based segmentation of coronary vascular structures , 2005, Fourth International Workshop on Volume Graphics, 2005..

[20]  E. Bolson,et al.  Lumen Diameter of Normal Human Coronary Arteries: Influence of Age, Sex, Anatomic Variation, and Left Ventricular Hypertrophy or Dilation , 1992, Circulation.

[21]  Martin Styner,et al.  Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms , 2009, Medical Image Anal..

[22]  Nico Bruining,et al.  Non-invasive visualization of coronary atherosclerosis: state-of-art , 2007, Journal of cardiovascular medicine.

[23]  Heinz-Otto Peitgen,et al.  Template-based multiple hypotheses tracking of small vessels , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[24]  Anthony J. Yezzi,et al.  Vessel Segmentation Using a Shape Driven Flow , 2004, MICCAI.

[25]  P.C. Johnson,et al.  Single-seeded coronary artery tracking in CT angiography , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[26]  Konstantin Mischaikow,et al.  Coronary vessel trees from 3D imagery: A topological approach , 2006, Medical Image Anal..

[27]  Anthony J. Yezzi,et al.  Vessels as 4-D Curves: Global Minimal 4-D Paths to Extract 3-D Tubular Surfaces and Centerlines , 2007, IEEE Transactions on Medical Imaging.

[28]  Heinz-Otto Peitgen,et al.  One-click coronary tree segmentation in CT angiographic images , 2005 .

[29]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[30]  Theo van Walsum,et al.  Bayesian Tracking of Tubular Structures and Its Application to Carotid Arteries in CTA , 2007, MICCAI.

[31]  Örjan Smedby,et al.  Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree , 2007, MICCAI.

[32]  Nikos Paragios,et al.  Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries , 2005, MICCAI.

[33]  Marcel Breeuwer,et al.  Minimum Cost Path Algorithm for Coronary Artery Central Axis Tracking in CT Images , 2003, MICCAI.

[34]  Don P. Giddens,et al.  AUTOMATIC SEGMENTATION OF CORONARY ARTERIES USING BAYESIAN DRIVEN IMPLICIT SURFACES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[35]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[36]  Cristian Lorenz,et al.  A General Framework for Tree Segmentation and Reconstruction from Medical Volume Data , 2004, MICCAI.

[37]  Stephen R. Aylward,et al.  Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction , 2002, IEEE Transactions on Medical Imaging.

[38]  Konstantin Nikolaou,et al.  Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound. , 2006, Journal of the American College of Cardiology.