Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation | |
Wang, Shuo1,3; Zhou, Mu2; Liu, Zaiyi4; Liu, Zhenyu1; Gu, Dongsheng1,3; Zang, Yali1,3; Dong, Di1,3; Gevaert, Olivier2; Tian, Jie1,3,5 | |
发表期刊 | MEDICAL IMAGE ANALYSIS |
2017-08-01 | |
卷号 | 40期号:40页码:172-183 |
文章类型 | Article |
摘要 | Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. (C) 2017 Published by Elsevier B.V. |
关键词 | Lung Nodule Segmentation Convolutional Neural Networks Deep Learning Computer-aided Diagnosis |
WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
DOI | 10.1016/j.media.2017.06.014 |
关键词[WOS] | THORACIC CT SCANS ; MR BRAIN IMAGES ; PULMONARY NODULES ; AUTOMATIC SEGMENTATION ; CONSORTIUM ; LESIONS |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(81227901 ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences(KFJ-SW-STS-160) ; special program for science and technology development from the Ministry of science and technology, China(2017YFA0205200 ; National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health(R01EB020527) ; Instrument Developing Project of the Chinese Academy of Sciences(YZ201502) ; Beijing Municipal Science and Technology Commission([Z161100002616022) ; Youth Innovation Promotion Association CAS ; 61231004 ; 2017YFC1308701 ; 81501616 ; 2017YFC1309100 ; 81671851 ; 2016CZYD0001) ; 81527805 ; 81501549) |
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:000407538000011 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20306 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Tian, Jie |
作者单位 | 1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 2.Stanford Univ, Dept Med, Stanford Ctr Biomed Informat Res BMIR, Stanford, CA 94305 USA 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Guangdong Gen Hosp, Guangzhou 510080, Guangdong, Peoples R China 5.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院分子影像重点实验室 |
通讯作者单位 | 中国科学院分子影像重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Shuo,Zhou, Mu,Liu, Zaiyi,et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation[J]. MEDICAL IMAGE ANALYSIS,2017,40(40):172-183. |
APA | Wang, Shuo.,Zhou, Mu.,Liu, Zaiyi.,Liu, Zhenyu.,Gu, Dongsheng.,...&Tian, Jie.(2017).Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.MEDICAL IMAGE ANALYSIS,40(40),172-183. |
MLA | Wang, Shuo,et al."Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation".MEDICAL IMAGE ANALYSIS 40.40(2017):172-183. |
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