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%.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging : First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings |
Editors | Fei Wang, Pingkun Yan, Kenji Suzuki, Dinggang Shen |
Number of pages | 8 |
Publisher | Springer |
Publication date | 2010 |
Pages | 42-49 |
ISBN (Print) | 978-3-642-15947-3 |
ISBN (Electronic) | 978-3-642-15948-0 |
DOIs | |
Publication status | Published - 2010 |
Event | 1st International Workshop on Machine Learning in Medical Imaging - Beijing, China Duration: 20 Sept 2010 → 20 Sept 2010 Conference number: 1 |
Conference
Conference | 1st International Workshop on Machine Learning in Medical Imaging |
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Number | 1 |
Country/Territory | China |
City | Beijing |
Period | 20/09/2010 → 20/09/2010 |
Series | Lecture notes in computer science |
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Volume | 6357 |
ISSN | 0302-9743 |