Automatic volumetry can reveal visually undetected disease features on brain MR images in temporal lobe epilepsy

  • Authors:

  • S. Keihaninejad;R. A. Heckemanna;I. S. Gousias;P. Aljabar;J. V. Hajnal;D. Rueckert;A. Hammers

  • Affiliations:

  • Imperial College London, London, UK;Imperial College London, London, UK and Neurodis Foundation, CERMEP, Lyon, France;Imperial College London, London, UK;Department of Computing, Imperial College London, London, UK;Imperial College London, London, UK and Neurodis Foundation, CERMEP, Lyon, France;Department of Computing, Imperial College London, London, UK;Imperial College London, London, UK and Neurodis Foundation, CERMEP, Lyon, France

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Brain structural volumes can be used for automatically classifying subjects into categories like controls and patients. We aimed to automatically separate patients with temporal lobe epilepsy (TLE) with and without hippocampal atrophy on MRI, pTLE and nTLE, from controls, and determine the epileptogenic side. In the proposed framework 83 brain structure volumes are identified using multi-atlas segmentation. We then use structure selection using a divergence measure and classification based on structural volumes, as well as morphological similarities using SVM. A spectral analysis step is used to convert the pairwise measures of similarity between subjects into per-subject features. Up to 96% of pTLE patients were correctly separated from controls using 14 structural brain volumes. The classification method based on spectral analysis was 91 % accurate at separating nTLE patients from controls. Right and left hippocampus were sufficient for the lateralization of the seizure focus in the pTLE group and achieved 100% accuracy.