Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases

• Published in: NeuroImage (Orlando, Fla.) Vol. 141; pp. 542 - 555
• Main Authors: Iglesias, Juan Eugenio, Van Leemput, Koen, Augustinack, Jean, Insausti, Ricardo, Fischl, Bruce, Reuter, Martin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2016; Elsevier Limited
• Subjects: Aged; Aging; Aging - pathology; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Atrophy; Bayes Theorem; Bayesian analysis; Bayesian modeling; Bias; Bioengineering; Cognitive ability; Computational neuroscience; Dentate gyrus; Female; Hippocampal subfields; Hippocampus; Hippocampus - diagnostic imaging; Hippocampus - pathology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Longitudinal modeling; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Memory; Middle Aged; Neurodegenerative diseases; Neuroimaging; Neurological disorders; Neurosciences; Pattern Recognition, Automated - methods; Pharmaceutical industry; Presubiculum; R&D; Registration; Reproducibility of Results; Research & development; Segmentation; Sensitivity and Specificity; Subiculum; Subtraction Technique; Longitudinal modeling; Hippocampal subfields; Segmentation; Bayesian modeling
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.07.020

The hippocampal formation is a complex, heterogeneous structure that consists of a number of distinct, interacting subregions. Atrophy of these subregions is implied in a variety of neurodegenerative diseases, most prominently in Alzheimer's disease (AD). Thanks to the increasing resolution of MR images and computational atlases, automatic segmentation of hippocampal subregions is becoming feasible in MRI scans. Here we introduce a generative model for dedicated longitudinal segmentation that relies on subject-specific atlases. The segmentations of the scans at the different time points are jointly computed using Bayesian inference. All time points are treated the same to avoid processing bias. We evaluate this approach using over 4700 scans from two publicly available datasets (ADNI and MIRIAD). In test–retest reliability experiments, the proposed method yielded significantly lower volume differences and significantly higher Dice overlaps than the cross-sectional approach for nearly every subregion (average across subregions: 4.5% vs. 6.5%, Dice overlap: 81.8% vs. 75.4%). The longitudinal algorithm also demonstrated increased sensitivity to group differences: in MIRIAD (69 subjects: 46 with AD and 23 controls), it found differences in atrophy rates between AD and controls that the cross sectional method could not detect in a number of subregions: right parasubiculum, left and right presubiculum, right subiculum, left dentate gyrus, left CA4, left HATA and right tail. In ADNI (836 subjects: 369 with AD, 215 with early cognitive impairment — eMCI — and 252 controls), all methods found significant differences between AD and controls, but the proposed longitudinal algorithm detected differences between controls and eMCI and differences between eMCI and AD that the cross sectional method could not find: left presubiculum, right subiculum, left and right parasubiculum, left and right HATA. Moreover, many of the differences that the cross-sectional method already found were detected with higher significance. The presented algorithm will be made available as part of the open-source neuroimaging package FreeSurfer. •A segmentation method for the hippocampal substructures in longitudinal MRI scans•Increased test–retest reliability compared with cross-sectional analysis•Increased power to detect group differences in atrophy rates in LME framework•Algorithm will be made publicly available as part of FreeSurfer