Segmentation of Subcortical Structures from Nonhuman Primate MRI

Abstract

Segmented analysis of subcortical structures within the nonhuman primate can potentially have a profound impact on studying the relationship between volumetric characteristics and alcohol dependencies. Image segmentations have been widely used in quantifying structural information. There are a variety of methods in which users can extract desired structures from a medical image; ranging from manual segmentations to fully-automated segmentations and 2-D to 3-D. The implications of this possibility can have tremendous applicability to medical research and diagnosis.

The primary goal of my thesis is to investigate different implementation methodologies for segmenting subcortical structures such as the hippocampus and striatum and then apply that knowledge towards the development of an approach to segment these two structures from a group of alcohol-dependent Rhesus Macaque monkeys. Using the Level Set Deformable Model (LSDM) with a priori structural information, a series of T1-weighted MR images of Rhesus Macaque hippocampi and striatum were segmented in an effort to compare the structural hippocampal and striatal volumes between early and late stages of alcohol dependency. The results suggest that the volumes of both subcortical structures are affected negatively by alcoholism. Volume deficits of as much as 5% for the hippocampus and 8% for the caudate were found.