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

作     者：Platero, Carlos Carmen Tobar, M. 

作者机构：Tech Univ Madrid Hlth Sci Technol Grp Ronda Valencia 3 Madrid 28012 Spain 

出 版 物：《NEUROINFORMATICS》 (神经信息学)

年 卷 期：2017年第15卷第2期

页      面：165-183页

核心收录：

学科分类：1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：ADNI (National Institutes of Health) [U01 AG024904] Laboratory of Cognitive and Computational Neuroscience (LCCN) of the Center of Biomedical Technology (Technical University of Madrid) National Institute on Aging National Institute of Biomedical Imaging and Bioengineering 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 Hoffmann-La Roche Ltd 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 Takeda Pharmaceutical Company 

主　　题：Atlas-based segmentation Image registration Patch-based label fusion Hippocampal segmentation Magnetic resonance imaging 

摘      要：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.