Department of Mathematics

Speaker: Polina Golland
Title: Modeling Heterogeneity in Anatomical Images
Affiliation: Massachusetts Institute of Technology
Date: Friday, February 4, 2011
Place and Time: Room 101, Love Building, 3:35-4:30 pm
Refreshments: Room 204, Love Building, 3:00 pm

Abstract. We consider the problem of capturing statistical variability in anatomical images. Unlike the classical approaches that model population variability with unimodal (Gaussian) distributions, we propose a richer set of mixture models better capable of capturing heterogeneous anatomy. Based on the model, we derive an efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to represent a population of images. The experimental results demonstrate that the algorithm can discover interesting sub-populations, suggesting applications in prior-guided segmentation and statistical analysis of anatomical differences in clinical studies.

Joint work with Mert Sabuncu and Serdar Balci.