Multiple hypothesis template tracking of small 3D vessel structures

A multiple hypothesis tracking approach to the segmentation of small 3D vessel structures is presented. By simultaneously tracking multiple hypothetical vessel trajectories, low contrast passages can be traversed, leading to an improved tracking performance in areas of low contrast. This work also contributes a novel mathematical vessel template model, with which an accurate vessel centerline extraction is obtained. The tracking is fast enough for interactive segmentation and can be combined with other segmentation techniques to form robust hybrid methods. This is demonstrated by segmenting both the liver arteries in CT angiography data, which is known to pose great challenges, and the coronary arteries in 32 CT cardiac angiography data sets in the Rotterdam Coronary Artery Algorithm Evaluation Framework, for which ground-truth centerlines are available.

[1]  M. Schaap,et al.  3D Segmentation in the Clinic: A Grand Challenge II - Coronary Artery Tracking , 2008, The MIDAS Journal.

[2]  Jack Lee,et al.  Automatic segmentation of 3D micro-CT coronary vascular images , 2007, Medical Image Anal..

[3]  Arnold W. M. Smeulders,et al.  High accuracy tracking of 2D/3D curved line-structures by consecutive cross-section matching , 1998, Pattern Recognit. Lett..

[4]  Ken Masamune,et al.  Effective Statistical Edge Integration Using a Flux Maximizing Scheme for Volumetric Vascular Segmentation in MRA , 2007, IPMI.

[5]  Nicholas Ayache,et al.  Model-Based Detection of Tubular Structures in 3D Images , 2000, Comput. Vis. Image Underst..

[6]  Guido Gerig,et al.  3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1997, CVRMed.

[7]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[8]  Ashraf A. Kassim,et al.  Segmentation of volumetric MRA images by using capillary active contour , 2006, Medical Image Anal..

[9]  Theo van Walsum,et al.  Bayesian Tracking of Elongated Structures in 3D Images , 2007, IPMI.

[10]  William E. Higgins,et al.  Symmetric region growing , 2003, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[11]  H. Peitgen,et al.  Coronary Centerline Extraction Using Multiple Hypothesis Tracking and Minimal Paths , 2008, The MIDAS Journal.

[12]  Kaleem Siddiqi,et al.  Flux maximizing geometric flows , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Karl Krissian,et al.  Smooth vasculature reconstruction with circular and elliptic cross sections. , 2006, Studies in health technology and informatics.

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

[15]  Karl Krissian,et al.  Flux-based anisotropic diffusion applied to enhancement of 3-D angiogram , 2002, IEEE Transactions on Medical Imaging.

[16]  Laurent D. Cohen,et al.  Minimal Paths in 3D Images and Application to Virtual Endoscopy , 2000, ECCV.

[17]  Alfred L. Nuttall,et al.  Matched filter estimation of serial blood vessel diameters from video images , 1993, IEEE Trans. Medical Imaging.

[18]  Katja Bühler,et al.  Geometric Methods for Vessel Visualization and Quantification — A Survey , 2004 .

[19]  Olivier D. Faugeras,et al.  CURVES: Curve evolution for vessel segmentation , 2001, Medical Image Anal..

[20]  P. Gill,et al.  Algorithms for the Solution of the Nonlinear Least-Squares Problem , 1978 .

[21]  Wiro J. Niessen,et al.  Local Speed Functions in Level Set Based Vessel Segmentation , 2004, MICCAI.

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

[23]  Åke Björck,et al.  Numerical methods for least square problems , 1996 .

[24]  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.

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

[26]  Karl Rohr,et al.  Segmentation and Quantification of Human Vessels Using a 3-D Cylindrical Intensity Model , 2007, IEEE Transactions on Image Processing.

[27]  Jian Chen,et al.  Quantifying 3-D vascular structures in MRA images using hybrid PDE and geometric deformable models , 2004, IEEE Transactions on Medical Imaging.

[28]  Norberto F. Ezquerra,et al.  Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial, and structural constraints , 1994, Other Conferences.

[29]  Laurent D. Cohen,et al.  Fast extraction of tubular and tree 3D surfaces with front propagation methods , 2002, Object recognition supported by user interaction for service robots.

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

[31]  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.

[32]  Badrinath Roysam,et al.  Robust 3-D Modeling of Vasculature Imagery Using Superellipsoids , 2007, IEEE Transactions on Medical Imaging.

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

[34]  Eduard Gröller,et al.  Non-linear model fitting to parameterize diseased blood vessels , 2004, IEEE Visualization 2004.

[35]  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..

[36]  Kecheng Liu,et al.  A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II , 2002, IEEE Transactions on Information Technology in Biomedicine.

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

[38]  Roland Wilson,et al.  Inferring Vascular Structure from 2D and 3D Imagery , 2001, MICCAI.

[39]  Wiro J. Niessen,et al.  Level set based cerebral vasculature segmentation and diameter quantification in CT angiography , 2006, Medical Image Anal..

[40]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[41]  Daniel Rueckert,et al.  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.

[42]  Ghassan Hamarneh,et al.  Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[43]  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.

[44]  Sang Uk Lee,et al.  Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering , 2006, Comput. Methods Programs Biomed..

[45]  Armin Kanitsar,et al.  Shape and Appearance Models for Automatic Coronary Artery Tracking , 2008, The MIDAS Journal.

[46]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[47]  Petia Radeva,et al.  Vesselness enhancement diffusion , 2003, Pattern Recognit. Lett..

[48]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Max A. Viergever,et al.  Vessel Axis Tracking Using Topology Constrained Surface Evolution , 2007, IEEE Transactions on Medical Imaging.

[50]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[51]  J. Rossignac,et al.  Pearling: 3D interactive extraction of tubular structures from volumetric images , 2007 .

[52]  Max A. Viergever,et al.  Vessel enhancing diffusion: A scale space representation of vessel structures , 2006, Medical Image Anal..

[53]  W. Hays.  Applied Regression Analysis. 2nd ed. , 1981 .

[54]  James S. Duncan,et al.  Medical Image Analysis , 1999, IEEE Pulse.

[55]  Karl Rohr,et al.  3D adaptive model-based segmentation of human vessels , 2007, SPIE Medical Imaging.

[56]  Karl Rohr,et al.  Robust segmentation of tubular structures in 3-D medical images by parametric object detection and tracking , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[57]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[58]  Isabelle Bloch,et al.  A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes , 2009, Medical Image Anal..

[59]  Fan Chung,et al.  Spectral Graph Theory , 1996 .