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Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation

文献类型：期刊论文

作者Zheng, Qiang1,2,3; Wu, Yihong3; Fan, Yong1
刊名FRONTIERS IN NEUROINFORMATICS
出版日期2018-10-10
卷号12页码:11
关键词multi-atlas image segmentation hippocampus random forests label propagation
ISSN号1662-5196
DOI10.3389/fninf.2018.00069
通讯作者Fan, Yong(yong.fan@uphs.upenn.edu)
英文摘要A 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.
WOS关键词REGISTRATION ; HIPPOCAMPUS ; SELECTION ; PROTOCOL ; ADNI
资助项目National 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]
WOS研究方向Mathematical & Computational Biology ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000446906900001
出版者FRONTIERS MEDIA SA
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/28133]  
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.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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