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Morphological hippocampal markers for automated detection of Alzheimer's disease and mild cognitive impairment converters in magnetic resonance images.
J Alzheimers Dis. 2009; 17(3):643-59.JA

Abstract

In this study, we investigated the use of hippocampal shape-based markers for automatic detection of Alzheimer's disease (AD) and mild cognitive impairment converters (MCI-c). Three-dimensional T1-weighted magnetic resonance images of 50 AD subjects, 50 age-matched controls, 15 MCI-c, and 15 MCI-non-converters (MCI-nc) were taken. Manual delineations of both hippocampi were obtained from normalized images. Fully automatic shape modeling was used to generate comparable meshes for both structures. Repeated permutation tests, run over a randomly sub-sampled training set (25 controls and 25 ADs), highlighted shape-based markers, mostly located in the CA1 sector, which consistently discriminated ADs and controls. Support vector machines (SVMs) were trained, using markers from either one or both hippocampi, to automatically classify control and AD subjects. Leave-1-out cross-validations over the remaining 25 ADs and 25 controls resulted in an optimal accuracy of 90% (sensitivity 92%), for markers in the left hippocampus. The same morphological markers were used to train SVMs for MCI-c versus MCI-nc classification: markers in the right hippocampus reached an accuracy (and sensitivity) of 80%. Due to the pattern recognition framework, our results statistically represent the expected performances of clinical set-ups, and compare favorably to analyses based on hippocampal volumes.

Authors+Show Affiliations

LKEB - Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands. L.Ferrarini@lumc.nlNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

19433888

Citation

Ferrarini, Luca, et al. "Morphological Hippocampal Markers for Automated Detection of Alzheimer's Disease and Mild Cognitive Impairment Converters in Magnetic Resonance Images." Journal of Alzheimer's Disease : JAD, vol. 17, no. 3, 2009, pp. 643-59.
Ferrarini L, Frisoni GB, Pievani M, et al. Morphological hippocampal markers for automated detection of Alzheimer's disease and mild cognitive impairment converters in magnetic resonance images. J Alzheimers Dis. 2009;17(3):643-59.
Ferrarini, L., Frisoni, G. B., Pievani, M., Reiber, J. H., Ganzola, R., & Milles, J. (2009). Morphological hippocampal markers for automated detection of Alzheimer's disease and mild cognitive impairment converters in magnetic resonance images. Journal of Alzheimer's Disease : JAD, 17(3), 643-59. https://doi.org/10.3233/JAD-2009-1082
Ferrarini L, et al. Morphological Hippocampal Markers for Automated Detection of Alzheimer's Disease and Mild Cognitive Impairment Converters in Magnetic Resonance Images. J Alzheimers Dis. 2009;17(3):643-59. PubMed PMID: 19433888.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Morphological hippocampal markers for automated detection of Alzheimer's disease and mild cognitive impairment converters in magnetic resonance images. AU - Ferrarini,Luca, AU - Frisoni,Giovanni B, AU - Pievani,Michela, AU - Reiber,Johan H C, AU - Ganzola,Rossana, AU - Milles,Julien, PY - 2009/5/13/entrez PY - 2009/5/13/pubmed PY - 2009/12/16/medline SP - 643 EP - 59 JF - Journal of Alzheimer's disease : JAD JO - J. Alzheimers Dis. VL - 17 IS - 3 N2 - In this study, we investigated the use of hippocampal shape-based markers for automatic detection of Alzheimer's disease (AD) and mild cognitive impairment converters (MCI-c). Three-dimensional T1-weighted magnetic resonance images of 50 AD subjects, 50 age-matched controls, 15 MCI-c, and 15 MCI-non-converters (MCI-nc) were taken. Manual delineations of both hippocampi were obtained from normalized images. Fully automatic shape modeling was used to generate comparable meshes for both structures. Repeated permutation tests, run over a randomly sub-sampled training set (25 controls and 25 ADs), highlighted shape-based markers, mostly located in the CA1 sector, which consistently discriminated ADs and controls. Support vector machines (SVMs) were trained, using markers from either one or both hippocampi, to automatically classify control and AD subjects. Leave-1-out cross-validations over the remaining 25 ADs and 25 controls resulted in an optimal accuracy of 90% (sensitivity 92%), for markers in the left hippocampus. The same morphological markers were used to train SVMs for MCI-c versus MCI-nc classification: markers in the right hippocampus reached an accuracy (and sensitivity) of 80%. Due to the pattern recognition framework, our results statistically represent the expected performances of clinical set-ups, and compare favorably to analyses based on hippocampal volumes. SN - 1875-8908 UR - https://www.unboundmedicine.com/medline/citation/19433888/Morphological_hippocampal_markers_for_automated_detection_of_Alzheimer's_disease_and_mild_cognitive_impairment_converters_in_magnetic_resonance_images_ L2 - https://content.iospress.com/openurl?genre=article&issn=1387-2877&volume=17&issue=3&spage=643 DB - PRIME DP - Unbound Medicine ER -