title: Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool.
abstract: In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice[Formula: see text] and Dice[Formula: see text]). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.
authors.affiliation: Dipartimento Interateneo di Fisica 'M Merlin', Università degli Studi di Bari 'Aldo Moro', 70121 Bari, Italy. Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy.
authors.forename: N
authors.initials: N
authors.lastname: Amoroso
authors.forename: R
authors.initials: R
authors.lastname: Errico
authors.forename: S
authors.initials: S
authors.lastname: Bruno
authors.forename: A
authors.initials: A
authors.lastname: Chincarini
authors.forename: E
authors.initials: E
authors.lastname: Garuccio
authors.forename: F
authors.initials: F
authors.lastname: Sensi
authors.forename: S
authors.initials: S
authors.lastname: Tangaro
authors.forename: A
authors.initials: A
authors.lastname: Tateo
authors.forename: R
authors.initials: R
authors.lastname: Bellotti
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated