Atlas-based classification of hyperintense regions from MR diffusion-weighted images of the brain: Preliminary results

Michel Bilello, Z. Lao, J. Krejza, A. E. Hillis, E. H. Herskovits

Research output: Contribution to journalArticle

Abstract

The study of subjects with acquired brain damage in a specific location is important in exploring human brain function. Description of lesion locations within and across subjects is a crucial methodological component that usually involves the distinction of normal from damaged tissue (lesion segmentation) in relation to lesion locations in terms of a standard anatomical reference space (lesion mapping). Our study provides an atlas-based, computer-aided methodology for classification of hyperintense regions on diffusion-weighted images of the brain, representing either ischemic lesions or susceptibility artifacts. We applied a leave-one-out method of cross-validation that computed probabilistic atlases of true lesions and artifacts, based on training data. Our approach accurately classifies lesions and artifacts, but leaves a significant number of regions unclassified, due to the relatively small number of training samples. An initial segmentation step based on a larger sample of data sets is required to automate discrimination of lesions and artifacts.

Original languageEnglish (US)
Pages (from-to)112-120
Number of pages9
JournalNeuroradiology Journal
Volume25
Issue number1
DOIs
StatePublished - Feb 2012

Keywords

  • Atlas
  • Classification
  • Diffusion
  • MRI
  • Stroke

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

Fingerprint Dive into the research topics of 'Atlas-based classification of hyperintense regions from MR diffusion-weighted images of the brain: Preliminary results'. Together they form a unique fingerprint.

  • Cite this