2010 | OriginalPaper | Buchkapitel
Prediction of Dementia by Hippocampal Shape Analysis
verfasst von : Hakim C. Achterberg, Fedde van der Lijn, Tom den Heijer, Aad van der Lugt, Monique M. B. Breteler, Wiro J. Niessen, Marleen de Bruijne
Erschienen in: Machine Learning in Medical Imaging
Verlag: Springer Berlin Heidelberg
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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%.