Semiautomatic Segmentation of Brain Subcortical Structures from High-Field MRI

Jinyoung Kim, Christophe Lenglet, Yuval Duchin, Guillermo Sapiro, Noam Harel

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6676825
Pages (from-to)1678-1695
Number of pages18
JournalIEEE Journal of Biomedical and Health Informatics
Volume18
Issue number5
DOIs
StatePublished - Sep 2014

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Basal ganglia and thalamus
  • deep brain stimulation (DBS)
  • geodesic active surface (GAS)
  • segmentation
  • ultrahigh field MRI

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