title: Computational analysis of LDDMM for brain mapping.
abstract: One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms-Freesurfer and FSL-by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.
authors.affiliation: Center for Imaging Science, The Johns Hopkins University Baltimore, MD, USA.
authors.forename: Can
authors.initials: C
authors.lastname: Ceritoglu
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Tang
authors.forename: Margaret
authors.initials: M
authors.lastname: Chow
authors.forename: Darian
authors.initials: D
authors.lastname: Hadjiabadi
authors.forename: Damish
authors.initials: D
authors.lastname: Shah
authors.forename: Timothy
authors.initials: T
authors.lastname: Brown
authors.forename: Muhammad H
authors.initials: MH
authors.lastname: Burhanullah
authors.forename: Huong
authors.initials: H
authors.lastname: Trinh
authors.forename: John T
authors.initials: JT
authors.lastname: Hsu
authors.forename: Katarina A
authors.initials: KA
authors.lastname: Ament
authors.forename: Deana
authors.initials: D
authors.lastname: Crocetti
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
authors.forename: Stewart H
authors.initials: SH
authors.lastname: Mostofsky
authors.forename: Steven
authors.initials: S
authors.lastname: Yantis
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.forename: J Tilak
authors.initials: JT
authors.lastname: Ratnanather
keywords: LDDMM; brain mapping; computational anatomy; subcortical segmentation