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Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation
Zhu, Hancan1; Tang, Zhenyu2; Cheng, Hewei3; Wu, Yihong4; Fan, Yong5
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
2019-11-14
Volume9Pages:14
Corresponding AuthorFan, Yong(yong.fan@uphs.upenn.edu)
AbstractAutomatic 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).
DOI10.1038/s41598-019-53387-9
WOS KeywordSPATIALLY VARYING PERFORMANCE ; AUTOMATED SEGMENTATION ; IMAGE SEGMENTATION ; VALIDATION ; SELECTION ; PATCH ; REGISTRATION ; STRATEGIES ; PARAMETERS ; VOLUMETRY
Indexed BySCI
Language英语
Funding ProjectTransition Therapeutics ; Servier ; Neurotrack Technologies ; Lundbeck ; Lumosity ; Janssen Alzheimer Immunotherapy Research & Development, LLC. ; IXICO Ltd. ; GE Healthcare ; Genentech, Inc. ; Eisai Inc. ; BioClinica, Inc. ; AbbVie ; Canadian Institutes of Health Research ; Takeda Pharmaceutical Company ; Piramal Imaging ; Pfizer Inc. ; Novartis Pharmaceuticals Corporation ; NeuroRx Research ; Meso Scale Diagnostics, LLC. ; Merck Co., Inc. ; Johnson & Johnson Pharmaceutical Research & Development LLC. ; Fujirebio ; F. Hoffmann-La Roche Ltd ; EuroImmun ; Eli Lilly and Company ; Elan Pharmaceuticals, Inc. ; Cogstate ; CereSpir, Inc. ; Bristol-Myers Squibb Company ; Biogen ; Araclon Biotech ; Alzheimer's Drug Discovery Foundation ; Alzheimer's Association ; National Institute of Biomedical Imaging and Bioengineering ; National Institute on Aging ; DOD ADNI (Department of Defense)[W81XWH-12-2-0012] ; Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)[U01 AG024904] ; Natural Science Foundation of Zhejiang Province[LY19F020013] ; National Institutes of Health[CA189523] ; National Institutes of Health[EB022573] ; National Natural Science Foundation of China[81271514] ; National Natural Science Foundation of China[61473296] ; National Natural Science Foundation of China[61502002] ; National Natural Science Foundation of China[61902047] ; National Natural Science Foundation of China[61877039] ; National Natural Science Foundation of China[61602307] ; National High Technology Research and Development Program of China[2015AA020504] ; National Key Basic Research and Development Program[2015CB856404] ; 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
Funding OrganizationNational 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 Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000496416000049
PublisherNATURE PUBLISHING GROUP
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28847
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorFan, Yong
Affiliation1.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
Recommended Citation
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.
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