CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
Effective automated pipeline for 3D reconstruction of synapses based on deep learning
Xiao, Chi1,2; Li, Weifu1,3; Deng, Hao4; Chen, Xi1; Yang, Yang5,6; Xie, QiWei1,7; Han, Hua1,2,6
Source PublicationBMC Bioinformatics
ISSN1471-2105
2018
Volume19Issue:1Pages:263
SubtypeMethodology Article
Abstract

Background: The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation. Results: We propose a fully automated method that relies on deep learning to realize the 3D reconstruction of synapses in electron microscopy (EM) images. The proposed method consists of three main parts: (1) training and employing the faster region convolutional neural networks (R-CNN) algorithm to detect synapses, (2) using the z-continuity of synapses to reduce false positives, and (3) combining the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. Experimental results were validated by manual tracking, and the effectiveness of our proposed method was demonstrated. The experimental results in anisotropic and isotropic EM volumes demonstrate the effectiveness of our algorithm, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results. Conclusions: Our fully automated approach contributes to the development of neuroscience, providing neurologists with a rapid approach for obtaining rich synaptic statistics.

KeywordElectron Microscope, Synapse Detection, Deep Learning, Synapse Segmentation, 3d Reconstruction Of Synapses
DOIhttps://doi.org/10.1186/s12859-018-2232-0
Indexed BySCIE
Language英语
Funding ProjectNational Natural Science Foundation of China[11771130] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146]
WOS IDBMC:10.1186/s12859-018-2232-0
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23633
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorXie, QiWei; Han, Hua
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
3.Faculty of Mathematics and Statistics, Hubei University, Hubei, China
4.Faculty of Information Technology, Macau University of Science and Technology, Macau, China
5.Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
6.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
7.Data Mining Lab, Beijing University of Technology, Beijing, 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, Chi,Li, Weifu,Deng, Hao,et al. Effective automated pipeline for 3D reconstruction of synapses based on deep learning[J]. BMC Bioinformatics,2018,19(1):263.
APA Xiao, Chi.,Li, Weifu.,Deng, Hao.,Chen, Xi.,Yang, Yang.,...&Han, Hua.(2018).Effective automated pipeline for 3D reconstruction of synapses based on deep learning.BMC Bioinformatics,19(1),263.
MLA Xiao, Chi,et al."Effective automated pipeline for 3D reconstruction of synapses based on deep learning".BMC Bioinformatics 19.1(2018):263.
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