title: Learning the dynamics and time-recursive boundary detection of deformable objects.
abstract: We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as nonparametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although this paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object.
authors.affiliation: Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. waltsun@alum.mit.edu
authors.forename: Walter
authors.initials: W
authors.lastname: Sun
authors.forename: Müjdat
authors.initials: M
authors.lastname: Cetin
authors.forename: Raymond
authors.initials: R
authors.lastname: Chan
authors.forename: Alan S
authors.initials: AS
authors.lastname: Willsky
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004548:Elasticity; D006352:Heart Ventricles / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity