Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning
2020-07-21
发表期刊FRONTIERS IN NEUROSCIENCE
EISSN1662-453X
卷号14
发表状态已发表
DOI10.3389/fnins.2020.00599
摘要Together, mitochondria and the endoplasmic reticulum (ER) occupy more than 20% of a cell's volume, and morphological abnormality may lead to cellular function disorders. With the rapid development of large-scale electron microscopy (EM), manual contouring and three-dimensional (3D) reconstruction of these organelles has previously been accomplished in biological studies. However, manual segmentation of mitochondria and ER from EM images is time consuming and thus unable to meet the demands of large data analysis. Here, we propose an automated pipeline for mitochondrial and ER reconstruction, including the mitochondrial and ER contact sites (MAMs). We propose a novel recurrent neural network to detect and segment mitochondria and a fully residual convolutional network to reconstruct the ER. Based on the sparse distribution of synapses, we use mitochondrial context information to rectify the local misleading results and obtain 3D mitochondrial reconstructions. The experimental results demonstrate that the proposed method achieves state-of-the-art performance.
关键词mitochondria endoplasmic reticulum electron microscopes segmentation 3D reconstruction
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收录类别SCI ; SCIE
语种英语
资助项目National Natural Science Foundation of China[61673381][31970960] ; Special Program of Beijing Municipal Science & Technology Commission[Z181100000118002] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32030200] ; Bureau of International Cooperation, CAS[153D31KYSB20170059] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; key program of the Ministry of Science and Technology of the People's Republic of China[2018YFC1005004]
WOS研究方向Neurosciences & Neurology
WOS类目Neurosciences
WOS记录号WOS:000558860100001
出版者FRONTIERS MEDIA SA
WOS关键词MITOFUSIN 2 ; DYNAMICS ; SEGMENTATION ; TRANSPORT ; SITES
原始文献类型Article
引用统计
文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/122944
专题生命科学与技术学院_PI研究组_杨扬组
通讯作者Xie, Qiwei; Han, Hua
作者单位
1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Sch Future Technol, Beijing, Peoples R China
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
4.Beijing Univ Technol, Data Min Lab, Beijing, Peoples R China
5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China
推荐引用方式
GB/T 7714
Liu, Jing,Li, Linlin,Yang, Yang,et al. Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning[J]. FRONTIERS IN NEUROSCIENCE,2020,14.
APA Liu, Jing.,Li, Linlin.,Yang, Yang.,Hong, Bei.,Chen, Xi.,...&Han, Hua.(2020).Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning.FRONTIERS IN NEUROSCIENCE,14.
MLA Liu, Jing,et al."Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning".FRONTIERS IN NEUROSCIENCE 14(2020).
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