<p>Volumetric segmentation of brain sub-cortical structures within the basal ganglia and thalamus from Magnetic Resonance Image (MRI) is necessary for non-invasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. With recent advances in ultrahigh-field MR technology, direct identification and clear visualization of such brain sub-cortical structures are facilitated. This dissertation first presents a semi-automatic segmentation system exploiting the visual benefits of ultrahigh-field MRI. The proposed approach utilizes the complementary edge information in the multiple structural MRI modalities. It combines optimally selected two modalities from susceptibility-weighted, T2-weighted, and diffusion MRI, and introduces a tailored new edge indicator function. In addition to this, prior shape and configuration knowledge of the sub-cortical structures are employed in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining over-segmentation at their borders with a non-overlapping penalty. Experiments with data acquired on a 7 Tesla (T) MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as Deep Brain Stimulation (DBS) surgery. </p><p>DBS surgery on brain sub-cortical regions within the Basal ganglia and thalamus is an effective treatment to alleviate symptoms of neuro-degenerative diseases. Particularly, the DBS of subthalamic nucleus (STN) has shown important clinical efficacy for Parkinson’s disease (PD). While accurate localization of the STN and its substructures is critical for precise DBS electrode placement, direct visualization of the STN in current standard clinical MR imaging (e.g., 1.5-3T) is still elusive. Therefore, to locate the target, DBS surgeons today often rely on consensus coordinates, lengthy and risky micro-electrode recording (MER), and patient’s behavioral feedback. Recently, ultrahigh-field MR imaging allows direct visualization of brain sub-cortical structures. However, such high fields are not clinically available in practice. This dissertation also introduces a non-invasive automatic localization method of the STN which is one of the critical targets for DBS surgery in a standard clinical scenario (1.5T MRI). The spatial dependency between the STN and potential predictor structures from 7T MR training data is first learned using the regression models in a bagging way. Then, given automatically detected such predictors on the clinical patient data, the complete region of the STN is predicted as a probability map using learned high quality information from 7T. Furthermore, a robust framework is proposed to properly weight different training subsets, estimating their influence in the prediction accuracy. The STN prediction on the clinical 1.5T MR datasets from 15 PD patients is performed within the proposed approach. Experimental results demonstrate that the developed framework enables accurate prediction of the STN, closely matching the 7T ground truth.</p> / Dissertation