Prediction of dementia by hippocampal shape analysis

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

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

Harvard

Achterberg, HC, van der Lijn, F, den Heijer, T, van der Lugt, A, Breteler, MMB, Niessen, WJ & de Bruijne, M 2010, Prediction of dementia by hippocampal shape analysis. in F Wang, P Yan, K Suzuki & D Shen (eds), Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. Springer, Lecture notes in computer science, vol. 6357, pp. 42-49, 1st International Workshop on Machine Learning in Medical Imaging, Beijing, China, 20/09/2010. https://doi.org/10.1007/978-3-642-15948-0_6

APA

Achterberg, H. C., van der Lijn, F., den Heijer, T., van der Lugt, A., Breteler, M. M. B., Niessen, W. J., & de Bruijne, M. (2010). Prediction of dementia by hippocampal shape analysis. In F. Wang, P. Yan, K. Suzuki, & D. Shen (Eds.), Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings (pp. 42-49). Springer. Lecture notes in computer science Vol. 6357 https://doi.org/10.1007/978-3-642-15948-0_6

Vancouver

Achterberg HC, van der Lijn F, den Heijer T, van der Lugt A, Breteler MMB, Niessen WJ et al. Prediction of dementia by hippocampal shape analysis. In Wang F, Yan P, Suzuki K, Shen D, editors, Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. Springer. 2010. p. 42-49. (Lecture notes in computer science, Vol. 6357). https://doi.org/10.1007/978-3-642-15948-0_6

Author

Achterberg, Hakim C. ; van der Lijn, Fedde ; den Heijer, Tom ; van der Lugt, Aad ; Breteler, Monique M. B. ; Niessen, Wiro J. ; de Bruijne, Marleen. / Prediction of dementia by hippocampal shape analysis. Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. editor / Fei Wang ; Pingkun Yan ; Kenji Suzuki ; Dinggang Shen. Springer, 2010. pp. 42-49 (Lecture notes in computer science, Vol. 6357).

Bibtex

@inproceedings{9ea1d910a18a11df928f000ea68e967b,
title = "Prediction of dementia by hippocampal shape analysis",
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%. ",
author = "Achterberg, {Hakim C.} and {van der Lijn}, Fedde and {den Heijer}, Tom and {van der Lugt}, Aad and Breteler, {Monique M. B.} and Niessen, {Wiro J.} and {de Bruijne}, Marleen",
year = "2010",
doi = "10.1007/978-3-642-15948-0_6",
language = "English",
isbn = "978-3-642-15947-3",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "42--49",
editor = "Fei Wang and Pingkun Yan and Kenji Suzuki and Dinggang Shen",
booktitle = "Machine Learning in Medical Imaging",
address = "Switzerland",
note = "1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010 ; Conference date: 20-09-2010 Through 20-09-2010",

}

RIS

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