Volumetric Segmentation of Multiple Basal Ganglia Structures
Mustafa Gokhan Uzunbas, Octavian Soldea, Mujdat Cetin, Gozde Unal, Aytul Ercil, Ahmet Ekin, Devrim Unay, Zeynep Firat
ISRACAS 2009 - the 12th Israeli Symposium on Computer-Aided Surgery, Medical Robotics, and Medical Imaging, ISRACAS 2009, 7th May, Israel
We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy.