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Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer’s Disease

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Abstract

We provide and evaluate an open-source software solution for automatically hippocampal segmentation from T1-weighted (T1w) magnetic resonance imaging (MRI). The method is applied for measuring the hippocampal volume, which allows discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC). The method is based on a fast patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances. These combined similarity measures produces better selection of the patches, and their weights are more robust. The algorithm is trained with the Harmonized Hippocampal Protocol (HarP). The proposal is compared with FreeSurfer and other label fusion methods. To evaluate the performance and the robustness of the proposed label fusion method, we employ two databases of T1w MRI of human brains. For AD vs NC, we obtain a high degree of accuracy, approximately 90 %. For MCI vs NC, we obtain accuracies around 75 %. The average time for the hippocampal segmentation from a T1w MRI is less than 17 minutes.

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Acknowledgments

Data collection for this study was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and the Laboratory of Cognitive and Computational Neuroscience (LCCN) of the Center of Biomedical Technology (Technical University of Madrid). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company.

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  1. Health Science Technology Group, Technical University of Madrid, Ronda de Valencia 3, 28012, Madrid, Spain

    Carlos Platero & M. Carmen Tobar

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Correspondence to Carlos Platero.

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Platero, C., Tobar, M.C. Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer’s Disease. Neuroinform 15, 165–183 (2017). https://doi.org/10.1007/s12021-017-9323-3

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