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Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation

文献类型：期刊论文

作者Zhu, Hancan4; Tang, Zhenyu1; Cheng, Hewei5; Wu, Yihong3; Fan, Yong2
刊名SCIENTIFIC REPORTS
出版日期2019-11-14
卷号9页码:14
ISSN号2045-2322
DOI10.1038/s41598-019-53387-9
通讯作者Fan, Yong(yong.fan@uphs.upenn.edu)
英文摘要Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer's Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen's d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer's disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multiatlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer's disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).
WOS关键词SPATIALLY VARYING PERFORMANCE ; AUTOMATED SEGMENTATION ; IMAGE SEGMENTATION ; VALIDATION ; SELECTION ; PATCH ; REGISTRATION ; STRATEGIES ; PARAMETERS ; VOLUMETRY
资助项目National Key Basic Research and Development Program[2015CB856404] ; National High Technology Research and Development Program of China[2015AA020504] ; National Natural Science Foundation of China[61602307] ; National Natural Science Foundation of China[61877039] ; National Natural Science Foundation of China[61902047] ; National Natural Science Foundation of China[61502002] ; National Natural Science Foundation of China[61473296] ; National Natural Science Foundation of China[81271514] ; National Institutes of Health[EB022573] ; National Institutes of Health[CA189523] ; Natural Science Foundation of Zhejiang Province[LY19F020013] ; Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)[U01 AG024904] ; DOD ADNI (Department of Defense)[W81XWH-12-2-0012] ; National Institute on Aging ; National Institute of Biomedical Imaging and Bioengineering ; Alzheimer's Association ; Alzheimer's Drug Discovery Foundation ; Araclon Biotech ; Biogen ; Bristol-Myers Squibb Company ; CereSpir, Inc. ; Cogstate ; Elan Pharmaceuticals, Inc. ; Eli Lilly and Company ; EuroImmun ; F. Hoffmann-La Roche Ltd ; Fujirebio ; Johnson & Johnson Pharmaceutical Research & Development LLC. ; Merck Co., Inc. ; Meso Scale Diagnostics, LLC. ; NeuroRx Research ; Novartis Pharmaceuticals Corporation ; Pfizer Inc. ; Piramal Imaging ; Takeda Pharmaceutical Company ; Canadian Institutes of Health Research ; AbbVie ; BioClinica, Inc. ; Eisai Inc. ; Genentech, Inc. ; GE Healthcare ; IXICO Ltd. ; Janssen Alzheimer Immunotherapy Research & Development, LLC. ; Lumosity ; Lundbeck ; Neurotrack Technologies ; Servier ; Transition Therapeutics
WOS研究方向Science & Technology - Other Topics
语种英语
出版者NATURE PUBLISHING GROUP
WOS记录号WOS:000496416000049
资助机构National Key Basic Research and Development Program ; National High Technology Research and Development Program of China ; National Natural Science Foundation of China ; National Institutes of Health ; Natural Science Foundation of Zhejiang Province ; Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) ; DOD ADNI (Department of Defense) ; National Institute on Aging ; National Institute of Biomedical Imaging and Bioengineering ; Alzheimer's Association ; Alzheimer's Drug Discovery Foundation ; Araclon Biotech ; Biogen ; Bristol-Myers Squibb Company ; CereSpir, Inc. ; Cogstate ; Elan Pharmaceuticals, Inc. ; Eli Lilly and Company ; EuroImmun ; F. Hoffmann-La Roche Ltd ; Fujirebio ; Johnson & Johnson Pharmaceutical Research & Development LLC. ; Merck Co., Inc. ; Meso Scale Diagnostics, LLC. ; NeuroRx Research ; Novartis Pharmaceuticals Corporation ; Pfizer Inc. ; Piramal Imaging ; Takeda Pharmaceutical Company ; Canadian Institutes of Health Research ; AbbVie ; BioClinica, Inc. ; Eisai Inc. ; Genentech, Inc. ; GE Healthcare ; IXICO Ltd. ; Janssen Alzheimer Immunotherapy Research & Development, LLC. ; Lumosity ; Lundbeck ; Neurotrack Technologies ; Servier ; Transition Therapeutics
源URL[http://ir.ia.ac.cn/handle/173211/28846]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Fan, Yong
作者单位1.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China
2.Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Zhejiang, Peoples R China
5.Chongqing Univ Posts & Telecommun, Sch Bioinformat, Dept Biomed Engn, Chongqing 400065, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Hancan,Tang, Zhenyu,Cheng, Hewei,et al. Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation[J]. SCIENTIFIC REPORTS,2019,9:14.
APA Zhu, Hancan,Tang, Zhenyu,Cheng, Hewei,Wu, Yihong,&Fan, Yong.(2019).Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation.SCIENTIFIC REPORTS,9,14.
MLA Zhu, Hancan,et al."Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation".SCIENTIFIC REPORTS 9(2019):14.

入库方式： OAI收割

来源：自动化研究所

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