CASIA OpenIR
Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation
Zheng, Qiang1,2,3; Wu, Yihong3; Fan, Yong1
Source PublicationFRONTIERS IN NEUROINFORMATICS
ISSN1662-5196
2018-10-10
Volume12Pages:11
Corresponding AuthorFan, Yong(yong.fan@uphs.upenn.edu)
AbstractA novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning basedmulti-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242.
Keywordmulti-atlas image segmentation hippocampus random forests label propagation
DOI10.3389/fninf.2018.00069
WOS KeywordREGISTRATION ; HIPPOCAMPUS ; SELECTION ; PROTOCOL ; ADNI
Indexed BySCI
Language英语
Funding ProjectNational Key Basic Research and Development Program of China[2015CB856404] ; National High Technology Research and Development Program of China[2015AA020504] ; National Natural Science Foundation of China[61473296] ; National Natural Science Foundation of China[61802330] ; China Postdoctoral Science Foundation[2015M581203] ; National Institutes of Health[CA223358] ; National Institutes of Health[EB022573] ; National Institutes of Health[DA039215] ; National Institutes of Health[DA039002] ; [20160032]
Funding OrganizationNational Key Basic Research and Development Program of China ; National High Technology Research and Development Program of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; National Institutes of Health
WOS Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000446906900001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28134
Collection中国科学院自动化研究所
Corresponding AuthorFan, Yong
Affiliation1.Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA
2.Yantai Univ, Sch Comp & Control Engn, Yantai, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zheng, Qiang,Wu, Yihong,Fan, Yong. Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation[J]. FRONTIERS IN NEUROINFORMATICS,2018,12:11.
APA Zheng, Qiang,Wu, Yihong,&Fan, Yong.(2018).Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation.FRONTIERS IN NEUROINFORMATICS,12,11.
MLA Zheng, Qiang,et al."Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation".FRONTIERS IN NEUROINFORMATICS 12(2018):11.
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