Prediction of dementia by hippocampal shape analysis

  • Authors:
  • Hakim C. Achterberg;Fedde Van Der Lijn;Tom Den Heijer;Aad Van Der Lugt;Monique M. B. Breteler;Wiro J. Niessen;Marleen De Bruijne

  • Affiliations:
  • Biomedical Imaging Group Rotterdam, Depts. of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Biomedical Imaging Group Rotterdam, Depts. of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Dept. of Epidemiology, Erasmus MC, Rotterdam, The Netherlands and Dept. of Neurology, Sint Franciscus Gasthuis, Rotterdam, The Netherlands;Dept. of Radiology, Erasmus MC, Rotterdam, The Netherlands;Dept. of Epidemiology, Erasmus MC, Rotterdam, The Netherlands;Biomedical Imaging Group Rotterdam, Depts. of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Imaging Science & Technology, Dept. of Applied Sciences, Delft Uni ...;Biomedical Imaging Group Rotterdam, Depts. of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Image Group, Dept. of Computer Science, University of Copenhagen, Cope ...

  • Venue:
  • MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
  • Year:
  • 2010

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Abstract

This work investigates the possibility of predicting future onset of dementia in subjects who are cognitively normal, using hippocampal shape and volume information extracted from MRI scans. A group of 47 subjects who were non-demented normal at the time of the MRI acquisition, but were diagnosed with dementia during a 9 year follow-up period, was selected from a large population based cohort study. 47 Age and gender matched subjects who stayed cognitively intact were selected from the same cohort study as a control group. The hippocampi were automatically segmented and all segmentations were inspected and, if necessary, manually corrected by a trained observer. From this data a statistical model of hippocampal shape was constructed, using an entropy-based particle system. This shape model provided the input for a Support Vector Machine classifier to predict dementia. Cross validation experiments showed that shape information can predict future onset of dementia in this dataset with an accuracy of 70%. By incorporating both shape and volume information into the classifier, the accuracy increased to 74%.