title: Geodesic distances to landmarks for dense correspondence on ensembles of complex shapes.
abstract: Establishing correspondence points across a set of biomedical shapes is an important technology for a variety of applications that rely on statistical analysis of individual subjects and populations. The inherent complexity (e.g. cortical surface shapes) and variability (e.g. cardiac chambers) evident in many biomedical shapes introduce significant challenges in finding a useful set of dense correspondences. Application specific strategies, such as registration of simplified (e.g. inflated or smoothed) surfaces or relying on manually placed landmarks, provide some improvement but suffer from limitations including increased computational complexity and ambiguity in landmark placement. This paper proposes a method for dense point correspondence on shape ensembles using geodesic distances to a priori landmarks as features. A novel set of numerical techniques for fast computation of geodesic distances to point sets is used to extract these features. The proposed method minimizes the ensemble entropy based on these features, resulting in isometry invariant correspondences in a very general, flexible framework.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, USA.
authors.forename: Manasi
authors.initials: M
authors.lastname: Datar
authors.affiliation: Department of Computer Science, University of North Carolina at Chapel Hill, USA.
authors.forename: Ilwoo
authors.initials: I
authors.lastname: Lyu
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel Hill, USA.
authors.forename: SunHyung
authors.initials: S
authors.lastname: Kim
authors.affiliation: CARMA Center, University of Utah, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Cates
authors.affiliation: Department of Computer Science, University of North Carolina at Chapel Hill, USA.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Styner
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, USA.
authors.forename: Ross
authors.initials: R
authors.lastname: Whitaker
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000033:anatomy & histology*; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity