Prediction of dementia by hippocampal shape analysis
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%.
|Persistent URL||dx.doi.org/10.1007/978-3-642-15948-0_6, hdl.handle.net/1765/27971|
Achterberg, H.C., van der Lijn, F., den Heijer, T., van der Lugt, A., Breteler, M.M.B., Niessen, W.J., & Bruijne, M.. (2010). Prediction of dementia by hippocampal shape analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6357 LNCS, 42–49. doi:10.1007/978-3-642-15948-0_6