Semi-automatic segmentation of brain sub-cortical structures from ultra high-field MRI

Project Summary

Volumetric segmentation of sub-cortical structures such as the basal ganglia and thalamus 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.

Themes and Categories
Imaging and the Brain
Year

This work presents a semi-automatic segmentation system exploiting the superior image quality of ultra-high field (7 Tesla) 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, we employ prior shape and configuration knowledge of the sub-cortical structures 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. Several experiments with data acquired on a 7T 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 surgery.

Related Projects

Exploring novel machine learning techniques for Brain Computer Interface (BCI) applications

A team of researchers associated with the Applied Machine Learning Lab in Duke’s ECE department will lead a team of students in developing novel machine learning techniques that will be used for improving brain computer interfaces (BCIs) using electroencephalography (EEG) data.  Students will learn how to pre-process EEG data, extract EEG features, and train machine learning algorithms for character selection in a spelling interface that allows “locked in” individuals, like Stephen Hawking, to communicate with the outside world.  In addition to developing machine learning algorithms, students will work to develop a dashboard to visualize EEG signals, trained classifier parameters, classifier outputs, and spelling decisions made by the BCI.

 

Project Lead: Leslie Collins

Project Manager: Evan Stump

Sub-thalamic nucleus (STN) location prediction based on ultra high-field MRI statistical shape relationships

The sub-thalamic nucleus (STN) within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Deep brain stimulation (DBS) surgery, in particular for alleviating Parkinson’s disease (PD) symptoms. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite process for reliable DBS targeting. While direct visualization and localization of the STN is facilitated with advanced high-field 7T MR imaging, such high fields are not always clinically available. 

See all Research