title: Automated detection of junctions structures and tracking of their trajectories in 4D images.
abstract: 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.
authors.affiliation: Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA. glxiong@stanford.edu
authors.forename: Guanglei
authors.initials: G
authors.lastname: Xiong
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*