Sulcal set optimization for cortical surface registration
Joshi AA, Pantazis D, Li Q, Damasio H, Shattuck DW, Toga AW, Leahy RM
Institution: Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1769 USA
Flat mapping based cortical surface registration constrained by manually traced sulcal curves has been widely used for inter subject comparisons of neuroanatomical data. Even for an experienced neuroanatomist, manual sulcal tracing can be quite time consuming, with the cost increasing with the number of sulcal curves used for registration. We present a method for estimation of an optimal subset of size NC from N possible candidate sulcal curves that minimizes a mean squared error metric over all combinations of NC curves. The resulting procedure allows us to estimate a subset with a reduced number of urves to be traced as part of the registration procedure leading to optimal use of manual labeling effort for registration. To minimize the
error metric we analyze the correlation structure of the errors in the sulcal curves by modeling them as a multivariate Gaussian distribution. For a given subset of sulci used as constraints in surface registration, the proposed model estimates registration error based on the correlation structure of the sulcal errors. The optimal subset of constraint curves consists of the NC sulci that jointly minimize the estimated error variance for the subset of unconstrained curves conditioned on the NC constraint curves. The optimal subsets of sulci
are presented and the estimated and actual registration errors for these subsets are computed.