title: Statistical shape analysis: From landmarks to diffeomorphisms.
abstract: We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for extracting and understanding patterns in large clinical image sets. We speculate on the future developments in shape analysis and potential applications that would bring this mathematically rich area to bear on clinical practice.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States.
authors.forename: Miaomiao
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States. Electronic address: polina@csail.mit.edu.
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D003952:Diagnostic Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical
keywords: Diffeomorphisms; Landmarks; Shape variability; Statistical shape analysis