CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
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
Source PublicationMEDICAL IMAGE ANALYSIS
2018
Volume43Issue:43Pages:98-111
SubtypeArticle
AbstractAccurate 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.
KeywordBrain Tumor Segmentation Fully Convolutional Neural Networks Conditional Random Fields Deep Learning
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.media.2017.10.002
WOS KeywordCONVOLUTIONAL NEURAL-NETWORKS ; IMAGE SEGMENTATION ; MRI IMAGES
Indexed BySCI
Language英语
Funding OrganizationNational High Technology Research and Development Program of China(2015AA020504) ; National Natural Science Foundation of China(61572499 ; NIH(EB022573 ; 61421004 ; CA189523) ; 61473296)
WOS Research AreaComputer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000418627400008
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19762
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.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
Recommended Citation
GB/T 7714
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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