Automated Detection of Junctions Structures and Tracking of Their Trajectories in 4D Images

Junction structures, as the natural anatomical markers, are useful to study the organ or tumor motion. However, detection and tracking of the junctions in four-dimensional (4D) images are challenging. The paper presents a novel framework to automate this task. Detection of their centers and sizes is first achieved by an analysis of local shape profiles on one segmented reference image. Junctions are then separately tracked by simultaneously using neighboring intensity features from all images. Defined by a closed B-spline space curve, the individual trajectory is assumed to be cyclic and obtained by maximizing the metric of combined correlation coefficients. Local extrema are suppressed by improving the initial conditions using random walks from pair-wise optimizations. Our approach has been applied to analyze the vessel junctions in five real 4D respiration-gated computed tomography (CT) image datasets with promising results. More than 500 junctions in the lung are detected with an average accuracy of greater than 85% and the mean error between the automated and the manual tracking is sub-voxel.

[1]  Calvin R. Maurer,et al.  A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Milan Sonka,et al.  Quantitative analysis of pulmonary airway tree structures , 2006, Comput. Biol. Medicine.

[3]  Max A. Viergever,et al.  Semi-automatic construction of reference standards for evaluation of image registration , 2011, Medical Image Anal..

[4]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[5]  Shinichi Minohara,et al.  Three-dimensional velocity mapping of lung motion using vessel bifurcation pattern matching. , 2006, Medical physics.

[6]  R. Castillo,et al.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets , 2009, Physics in medicine and biology.

[7]  R. Castillo,et al.  Four-dimensional deformable image registration using trajectory modeling , 2010, Physics in medicine and biology.

[8]  Stefan Klein,et al.  Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach , 2011, Medical Image Anal..

[9]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[10]  Paul Suetens,et al.  Robust matching of 3D lung vessel trees , 2010 .

[11]  J. Nocedal.  Updating Quasi-Newton Matrices With Limited Storage , 1980 .

[12]  Steve B. Jiang,et al.  4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis. , 2008, Medical physics.

[13]  Bram van Ginneken,et al.  Computer analysis of computed tomography scans of the lung: a survey , 2006, IEEE Transactions on Medical Imaging.

[14]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[15]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

[16]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..