Feature-based, automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging.

  • Braun J
  • Bernarding J
  • Koennecke H
 et al. 
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Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.

Author-supplied keywords

  • Algorithms
  • Cerebral Infarction
  • Cerebral Infarction: classification
  • Cerebral Infarction: diagnosis
  • Cluster Analysis
  • Computer Simulation
  • Diffusion Magnetic Resonance Imaging
  • Diffusion Magnetic Resonance Imaging: methods
  • Humans
  • Image Enhancement
  • Image Enhancement: methods
  • Image Interpretation, Computer-Assisted
  • Image Interpretation, Computer-Assisted: methods
  • Imaging, Three-Dimensional
  • Imaging, Three-Dimensional: methods
  • Pattern Recognition, Automated
  • Pilot Projects
  • Reproducibility of Results
  • Sensitivity and Specificity

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  • Juergen Braun

  • Johannes Bernarding

  • Hans-Christian Koennecke

  • Karl-Juergen Wolf

  • Thomas Tolxdorff

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