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%.
Originalsprog | Engelsk |
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Titel | Machine Learning in Medical Imaging : First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings |
Redaktører | Fei Wang, Pingkun Yan, Kenji Suzuki, Dinggang Shen |
Antal sider | 8 |
Forlag | Springer |
Publikationsdato | 2010 |
Sider | 42-49 |
ISBN (Trykt) | 978-3-642-15947-3 |
ISBN (Elektronisk) | 978-3-642-15948-0 |
DOI | |
Status | Udgivet - 2010 |
Begivenhed | 1st International Workshop on Machine Learning in Medical Imaging - Beijing, Kina Varighed: 20 sep. 2010 → 20 sep. 2010 Konferencens nummer: 1 |
Konference
Konference | 1st International Workshop on Machine Learning in Medical Imaging |
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Nummer | 1 |
Land/Område | Kina |
By | Beijing |
Periode | 20/09/2010 → 20/09/2010 |
Navn | Lecture notes in computer science |
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Vol/bind | 6357 |
ISSN | 0302-9743 |