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An Effective Encoder-Decoder Network for Neural Cell Bodies and Cell Nucleus Segmentation of EM Images
Jiang Yi1; Xiao Chi1; Li Linlin1; Shen Lijun1; Chen Xi1; Han Hua2,3,4
2019-07
Conference NameIn2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pages6302-6305
Conference Date2019-7
Conference Place德国柏林
Country德国
PublisherIEEE
Contribution Rank1
Abstract

Neural systems are complicated networks connected by a large number of neurons through gap junctions and synapses. At present, for electron microscopy connectomics research, neuron structure recognition algorithms mostly focus on synapses, dendrites, axons and mitochondria, etc. However, effective methods for automatic recognition of neuronal cell bodies are rare. In this paper, we proposed an effective encoder-decoder network, which extracted segmentation features of neural cell bodies and cell nuclei by the modified residual network and pyramid module. The framework is capable of merging multi-scale contextual information and generating efficient segmentation results by integrating multilevel features. We applied this proposed network on two segmentation tasks for electron microscope (EM) images and compared it with other promising methods as U-Net and deeplab v3+. The results demonstrated that our method achieved state-of-the-art performance on quality metrics. Finally, we visualized two intact neural cell bodies and cell nuclei to provide a close look into these fine structures.

KeywordEncoder-Decoder Electron Microscopy Neural Cell Bodies Cell Nucleus Image Segmentation
Subject Area计算机科学技术 ; 人工智能
MOST Discipline Catalogue工学::控制科学与工程
DOI10.1109/EMBC.2019.8857887
Indexed ByEI
Funding ProjectStrategic Priority Research Program of Chinese Academy of Science[XDB32030200] ; Special Program of Beijing Municipal Science & Technology Commission[Z181100000118002] ; National Science Foundation of China[61701497] ; National Science Foundation of China[61673381] ; Scientific Research Instrument and Equipment Development Project of the CAS[YZ201671]
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48521
Collection类脑智能研究中心_微观重建与智能分析
Corresponding AuthorHan Hua
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
4.School of Future technology, University of Chinese Academy of Sciences, Beijing 101408, China.
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jiang Yi,Xiao Chi,Li Linlin,et al. An Effective Encoder-Decoder Network for Neural Cell Bodies and Cell Nucleus Segmentation of EM Images[C]:IEEE,2019:6302-6305.
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