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Automatic segmentation of intracerebral hemorrhage in CT images using encoder-decoder convolutional neural network

文献类型：期刊论文

作者Hu, Kai5; Chen, Kai4; He, Xizhi5; Zhang, Yuan5; Chen, Zhineng3; Li, Xuanya2; Gao, Xieping1,5
刊名INFORMATION PROCESSING & MANAGEMENT
出版日期2020-11-01
卷号57期号:6页码:16
关键词Intracerebral hemorrhage Segmentation Convolutional neural networks Multi-scale features Data imbalance
ISSN号0306-4573
DOI10.1016/j.ipm.2020.102352
通讯作者Chen, Zhineng(zhineng.chen@ia.ac.cn) ; Gao, Xieping(xpgao@xtu.edu.cn)
英文摘要Intracerebral hemorrhage (ICH) is the most serious type of stroke, which results in a high disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great significance for clinical diagnosis and treatment of ICH. In this paper, we focus on deep neural networks to automatically segment ICH regions. Firstly, we propose an encoder-decoder convolutional neural network (ED-Net) architecture to comprehensively utilizing both the low-level and high-level semantic information. Specifically, the encoder is used to extract multi-scale semantic feature information, while the decoder integrates them to form a unified ICH feature representation. Secondly, we introduce a synthetic loss function by paying more attention to the small ICH regions to overcome the data imbalanced problem. Thirdly, to improve the clinical adaptability of the proposed model, we collect 480 patient cases with ICH from four hospitals to construct a multi-center dataset, in which each case contains the first and review CT scans. In particular, CT scans of different patients are diverse, which greatly increases the difficulty of segmentation. Finally, we evaluate ED-Net on the multi-center ICH clinical dataset from different model parameters and different loss functions. We also compare the results of ED-Net with nine state-of-the-art methods in the literature. Both quantitative and visual results have shown that ED-Net outperforms other methods by providing more accurate and stable performance.
WOS关键词INTRACRANIAL HEMORRHAGE ; BRAIN
资助项目National Natural Science Foundation of China[61802328] ; National Natural Science Foundation of China[61972333] ; National Natural Science Foundation of China[61771415] ; Natural Science Foundation of Hunan Province of China[2019JJ50606] ; Research Foundation of Education Department of Hunan Province of China[19B561] ; Health and Family Planning Commission of Hunan Province[20200068]
WOS研究方向Computer Science ; Information Science & Library Science
语种英语
WOS记录号WOS:000582206800066
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hunan Province of China ; Research Foundation of Education Department of Hunan Province of China ; Health and Family Planning Commission of Hunan Province
源URL[http://ir.ia.ac.cn/handle/173211/41750]  
专题数字内容技术与服务研究中心_远程智能医疗
通讯作者Chen, Zhineng; Gao, Xieping
作者单位1.Xiangnan Univ, Coll Med Imaging & Inspect, Chenzhou 423000, Peoples R China
2.Baidu Inc, Beijing 100085, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Xiangnan Univ, Dept Radiol, Affiliated Hosp, Cherushou 423000, Peoples R China
5.Xiangtan Univ, Key Lab Intelligent Comp & Informat Proc, Minist Educ, Xiangtan 411105, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Kai,Chen, Kai,He, Xizhi,et al. Automatic segmentation of intracerebral hemorrhage in CT images using encoder-decoder convolutional neural network[J]. INFORMATION PROCESSING & MANAGEMENT,2020,57(6):16.
APA Hu, Kai.,Chen, Kai.,He, Xizhi.,Zhang, Yuan.,Chen, Zhineng.,...&Gao, Xieping.(2020).Automatic segmentation of intracerebral hemorrhage in CT images using encoder-decoder convolutional neural network.INFORMATION PROCESSING & MANAGEMENT,57(6),16.
MLA Hu, Kai,et al."Automatic segmentation of intracerebral hemorrhage in CT images using encoder-decoder convolutional neural network".INFORMATION PROCESSING & MANAGEMENT 57.6(2020):16.

入库方式： OAI收割

来源：自动化研究所

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