Knowledge Commons of Institute of Automation,CAS
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation | |
Zhao, Xiaomei1,2![]() ![]() | |
发表期刊 | MEDICAL IMAGE ANALYSIS
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ISSN | 1361-8415 |
2018 | |
卷号 | 43期号:-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 |
DOI | 10.1016/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 | 多模态人工智能系统全国重点实验室_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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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(-):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(-),98-111. |
MLA | Zhao, Xiaomei,et al."A deep learning model integrating FCNNs and CRFs for brain tumor segmentation".MEDICAL IMAGE ANALYSIS 43.-(2018):98-111. |
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4. Medical Image Ana(1963KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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