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Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics

文献类型：会议论文

作者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
出版日期2018-06
会议日期美国华盛顿
会议地点2018-4
关键词Connectomics, Deep Learning, Image Segmentation, Electron Microscopy
DOI10.1109/ISBI.2018.8363597
英文摘要
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.
语种英语
资助项目National 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]
源URL[http://ir.ia.ac.cn/handle/173211/23697]  
专题类脑智能研究中心_微观重建与智能分析
自动化研究所_类脑智能研究中心
通讯作者Han H(韩华)
作者单位1.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
推荐引用方式
GB/T 7714		 Xiao C,Liu J,Chen X,et al. Deep Contextual Residual Network for Electron Microscopy Image Segmentation in Connectomics[C]. 见:. 2018-4. 美国华盛顿.

入库方式： OAI收割

来源：自动化研究所

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