Prediction of dementia by hippocampal shape analysis
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Prediction of dementia by hippocampal shape analysis. / Achterberg, Hakim C.; van der Lijn, Fedde; den Heijer, Tom; van der Lugt, Aad; Breteler, Monique M. B.; Niessen, Wiro J.; de Bruijne, Marleen.
Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. ed. / Fei Wang; Pingkun Yan; Kenji Suzuki; Dinggang Shen. Springer, 2010. p. 42-49 (Lecture notes in computer science, Vol. 6357).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Prediction of dementia by hippocampal shape analysis
AU - Achterberg, Hakim C.
AU - van der Lijn, Fedde
AU - den Heijer, Tom
AU - van der Lugt, Aad
AU - Breteler, Monique M. B.
AU - Niessen, Wiro J.
AU - de Bruijne, Marleen
N1 - Conference code: 1
PY - 2010
Y1 - 2010
N2 - 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%.
AB - 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%.
U2 - 10.1007/978-3-642-15948-0_6
DO - 10.1007/978-3-642-15948-0_6
M3 - Article in proceedings
SN - 978-3-642-15947-3
T3 - Lecture notes in computer science
SP - 42
EP - 49
BT - Machine Learning in Medical Imaging
A2 - Wang, Fei
A2 - Yan, Pingkun
A2 - Suzuki, Kenji
A2 - Shen, Dinggang
PB - Springer
T2 - 1st International Workshop on Machine Learning in Medical Imaging
Y2 - 20 September 2010 through 20 September 2010
ER -
ID: 21235793