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

doi:10.1038/s41598-019-53387-9

	Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation
	Zhu, Hancan 1; Tang, Zhenyu 2; Cheng, Hewei 3; Wu, Yihong4 ; Fan, Yong 5
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
	2019-11-14
卷号	9 页码:14
通讯作者	Fan, Yong(yong.fan@uphs.upenn.edu)
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).
DOI	10.1038/s41598-019-53387-9
关键词[WOS]	SPATIALLY VARYING PERFORMANCE ; AUTOMATED SEGMENTATION ; IMAGE SEGMENTATION ; VALIDATION ; SELECTION ; PATCH ; REGISTRATION ; STRATEGIES ; PARAMETERS ; VOLUMETRY
收录类别	SCI
语种	英语
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 ; 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
项目资助者	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
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000496416000049
出版者	NATURE PUBLISHING GROUP
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28847
专题	多模态人工智能系统全国重点实验室_机器人视觉
通讯作者	Fan, Yong
作者单位	1.Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Zhejiang, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China 3.Chongqing Univ Posts & Telecommun, Sch Bioinformat, Dept Biomed Engn, Chongqing 400065, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA
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.