Semiautomatic segmentation of brain subcortical structures from high-field MRI.

Published

Journal Article

Volumetric segmentation of subcortical structures, such as the basal ganglia and thalamus, is necessary for noninvasive 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. This paper presents a semiautomatic segmentation system exploiting the superior image quality of ultrahigh field (7 T) 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 subcortical structures in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining oversegmentation at their borders with a nonoverlapping penalty. Several experiments with data acquired on a 7 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 surgery.

Full Text

Duke Authors

Cited Authors

  • Kim, J; Lenglet, C; Duchin, Y; Sapiro, G; Harel, N

Published Date

  • September 2014

Published In

Volume / Issue

  • 18 / 5

Start / End Page

  • 1678 - 1695

PubMed ID

  • 25192576

Pubmed Central ID

  • 25192576

Electronic International Standard Serial Number (EISSN)

  • 2168-2208

International Standard Serial Number (ISSN)

  • 2168-2194

Digital Object Identifier (DOI)

  • 10.1109/JBHI.2013.2292858

Language

  • eng