Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI Harmonized Hippocampal Protocol.
The hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. Here we present a novel method for the automated segmentation of the hippocampus from structural magnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validated on a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, and Alzheimer's Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol training labels is used as gold standard. The fully automated pipeline consists of a registration using an affine transformation, the extraction of a local bounding box, and the classification of each voxel in two classes (background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonal directions of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the three full segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtained by a single monolithic RF applied to the entire bounding box, and are comparable to state-of-the-art. A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust and reliable. Additionally, a comparison of local changes in the morphology of the hippocampi between the three subject groups is performed. Our work showed that a multiple classification approach can be implemented for the segmentation for the measurement of volume and shape changes of the hippocampus with diagnostic purposes.
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy; Università degli Studi di Bari, Bari, Italy.
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Inglese
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy; Università degli Studi di Bari, Bari, Italy.
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Amoroso
LENITEM Laboratory of Epidemiology, Neuroimaging & Telemedicine, IRCSS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy.
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Boccardi
LENITEM Laboratory of Epidemiology, Neuroimaging & Telemedicine, IRCSS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
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Bocchetta
Overdale Hospital, St Helier, Jersey, UK.
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Bruno
Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Italy.
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Chincarini
Università degli Studi di Bari, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Italy; Università degli Studi di Genova, Genova, Italy.
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Errico
LENITEM Laboratory of Epidemiology, Neuroimaging & Telemedicine, IRCSS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy; aFaR Associazione FateBeneFratelli per la Ricerca, Rome, Italy; Psychogeriatric Ward, IRCSS S. Giovanni di Dio - FBF, Brescia, Italy.
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Frisoni
Istituto di Studi sui Sistemi Intelligenti per l'Automazione, Consiglio Nazionale delle Ricerche, Bari, Italy.
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Maglietta
LENITEM Laboratory of Epidemiology, Neuroimaging & Telemedicine, IRCSS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy.
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Redolfi
Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Italy.
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Sensi
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy. Electronic address: sonia.tangaro@ba.infn.it.
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Tangaro
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy; Università degli Studi di Bari, Bari, Italy.
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Tateo
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy; Università degli Studi di Bari, Bari, Italy.
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Bellotti
D000465:Algorithms; D006624:Hippocampus; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging
Alzheimer's disease; Hippocampus segmentation; Random forest classifier