CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics
Xiao C(肖驰)1,2; Liu J(刘静)1,2; Chen X(陈曦)1; Han H(韩华)1,2,3; Shu C(舒畅)1; Xie QW(谢启伟)1
2018-06
Conference NameIEEE International Symposium on Biomedical Imaging (ISBI)
Conference Date美国华盛顿
Conference Place2018-4
Abstract
The goal of connectomics research is to manifest the mechanisms and functions of neural system by using electron microscopy (EM). One of the biggest challenges in connectomic reconstruction is developing reliable neuronal membranes segmentation method to reduce the burden on manual neurite labeling and validation. In this paper, we put forward an effective deep learning approach to realize neuronal membranes segmentation in EM image stacks, which utilizes spatially efficient residual network and multilevel representations of contextual cues to achieve accurate segmentation performance. Furthermore, multicut is used as post-processing to optimize the outputs of network. Experimental results on the public dataset of ISBI 2012 EM Segmentation Challenge demonstrate the effectiveness of our approach in neuronal membranes segmentation. Our method now ranks top 3 among 88 teams and yields 0.98356 Rand Score as well as 0.99063 Information Score, which outperforms most of state-of-the-art methods.
KeywordConnectomics, Deep Learning, Image Segmentation, Electron Microscopy
DOI10.1109/ISBI.2018.8363597
Indexed ByEI
Funding ProjectNational Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[31472001] ; Strategic Priority Research Program of the CAS[XDB02060001] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146]
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23697
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorHan H(韩华)
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
3.The Center for Excellence in Brain Science and Intelligence Technology, CAS, Shanghai, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xiao C,Liu J,Chen X,et al. Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics[C],2018.
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