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.
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.