CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
3D reconstruction of synapses with deep learning based on EM Images
Xiao C(肖驰)1; Rao Q(饶强)1; Zhang DD(张丹丹)1; Chen X(陈曦)1; Han H(韩华)1,2; Xie QW(谢启伟)1
2017-03
Conference NameSPIE Medical Imaging
Conference Date2017-2
Conference Place美国奥兰多
Abstract

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tape-collecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.

KeywordScanning Electron Microscope, Deep Learning, 3d Reconstruction Of Synapses
DOIhttps://doi.org/10.1117/12.2254282
Indexed ByEI
Funding ProjectNational Natural Science Foundation of China[61673381] ; Strategic Priority Research Program of the CAS[XDB02060001]
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23694
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorHan H(韩华)
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.The Center for Excellence in B rain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 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 C,Rao Q,Zhang DD,et al. 3D reconstruction of synapses with deep learning based on EM Images[C],2017.
Files in This Item: Download All
File Name/Size DocType Version Access License
3D reconstruction of(1006KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xiao C(肖驰)]'s Articles
[Rao Q(饶强)]'s Articles
[Zhang DD(张丹丹)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xiao C(肖驰)]'s Articles
[Rao Q(饶强)]'s Articles
[Zhang DD(张丹丹)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xiao C(肖驰)]'s Articles
[Rao Q(饶强)]'s Articles
[Zhang DD(张丹丹)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 3D reconstruction of synapses with deep learning based on EM Images.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.