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 | |
发表期刊 | BMC Bioinformatics |
ISSN | 1471-2105 |
2018 | |
卷号 | 19期号:1页码:263 |
文章类型 | Methodology Article |
摘要 | 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. |
关键词 | Electron Microscope, Synapse Detection, Deep Learning, Synapse Segmentation, 3d Reconstruction Of Synapses |
DOI | https://doi.org/10.1186/s12859-018-2232-0 |
收录类别 | SCIE |
语种 | 英语 |
资助项目 | Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; National Natural Science Foundation of China[11771130] ; National 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记录号 | BMC:10.1186/s12859-018-2232-0 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23633 |
专题 | 类脑智能研究中心_微观重建与智能分析 |
通讯作者 | Xie, QiWei; Han, Hua |
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.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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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