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A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, Xiaomei1,2; Wu, Yihong1; Song, Guidong3; Li, Zhenye4; Zhang, Yazhuo3,4,5,6; Fan, Yong7
2018
发表期刊MEDICAL IMAGE ANALYSIS
卷号43期号:43页码:98-111
文章类型Article
摘要Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FC-NNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans. (C) 2017 Elsevier B.V. All rights reserved.
关键词Brain Tumor Segmentation Fully Convolutional Neural Networks Conditional Random Fields Deep Learning
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.media.2017.10.002
关键词[WOS]CONVOLUTIONAL NEURAL-NETWORKS ; IMAGE SEGMENTATION ; MRI IMAGES
收录类别SCI
语种英语
项目资助者National High Technology Research and Development Program of China(2015AA020504) ; National Natural Science Foundation of China(61572499 ; NIH(EB022573 ; 61421004 ; CA189523) ; 61473296)
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000418627400008
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19762
专题模式识别国家重点实验室_机器人视觉
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Capital Med Univ, Beijing Neurosurg Inst, Beijing, Peoples R China
4.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China
5.Beijing Inst Brain Disorders Brain Tumor Ctr, Beijing, Peoples R China
6.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China
7.Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA
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Zhao, Xiaomei,Wu, Yihong,Song, Guidong,et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation[J]. MEDICAL IMAGE ANALYSIS,2018,43(43):98-111.
APA Zhao, Xiaomei,Wu, Yihong,Song, Guidong,Li, Zhenye,Zhang, Yazhuo,&Fan, Yong.(2018).A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.MEDICAL IMAGE ANALYSIS,43(43),98-111.
MLA Zhao, Xiaomei,et al."A deep learning model integrating FCNNs and CRFs for brain tumor segmentation".MEDICAL IMAGE ANALYSIS 43.43(2018):98-111.
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