| 脑微观重建系统研究 |
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| 引用本文： | 谢启伟,陈曦,沈丽君,李国庆,马宏图,韩华. 脑微观重建系统研究[J]. 系统工程理论与实践, 2017, 37(11): 3006-3017. DOI: 10.12011/1000-6788(2017)11-3006-12 |
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| 作者姓名： | 谢启伟 陈曦 沈丽君 李国庆 马宏图 韩华 |
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| 作者单位： | 1. 北京工业大学 经济与管理学院, 北京 100124;2. 中国科学院 自动化研究所, 北京 100190;3. 中国科学院 脑科学与智能技术卓越创新中心, 上海 200031;4. 中国科学院大学 未来技术学院, 北京 101407 |
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| 基金项目： | 国家自然科学基金（61673381,61201050,61306070）;北京市科委项目（Z161100000216146）;中国科学院先导项目（XDB02060001）;中国科学院自动化研究所突触级三维重建项目（Y3J2031DZ1）;中国科学院科研仪器设备研制项目（YZ201671） |
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| 摘 要： | 本文旨在研究如何利用硬件,软件,算法的系统工程的方案解决脑微观结构重建的高通量自动化数据分析的难题.通过实现样片制备,自动切片,显微成像,三维重建以及软件平台等分阶段工作,建立符合神经结构生物学特征的模式识别和机器学习分类方法,解决海量畸变图像拼接配准,密集神经结构识别,歧义神经联结,多源数据融合等限制分析效率的关键问题,为搭建大体量神经结构重建工程平台提供理论基础和解决方案,满足脑科学研究对高通量神经回路网络重建的需求.
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| 关 键 词： | 脑计划 神经回路重建 机器学习 深度学习 图像配准 特征提取 |
| 收稿时间： | 2016-09-13 |
Micro reconstruction system for brain |
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| XIE Qiwei,CHEN Xi,SHEN Lijun,LI Guoqing,MA Hongtu,HAN Hua. Micro reconstruction system for brain[J]. Systems Engineering —Theory & Practice, 2017, 37(11): 3006-3017. DOI: 10.12011/1000-6788(2017)11-3006-12 |
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| Authors: | XIE Qiwei CHEN Xi SHEN Lijun LI Guoqing MA Hongtu HAN Hua |
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| Affiliation: | 1. School of Economics and Management, Beijing University of Technology, Beijing 100124, China;2. 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. Future Technical College, University of Chinese Academy of Sciences, Beijing 101407, China |
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| Abstract: | This paper aims to solve the problem of high throughput automated data analysis of brain microstructure reconstruction by using the solution of systems engineering that consists of hardware, software and algorithm. To solve the key issues of limit analysis efficiency such as registration of large-volume distorted image, dense neural structure recognition, ambiguous neural connections and multi-source data fusion etc, classification method in accordance with the biological characteristics of neural structure is proposed by the realization of the samples preparation, automatic cutting, microscopic imaging, 3D reconstruction and software platform building etc. Furthermore, it provides a theoretical basis and solution for building engineering platform for large volume neural structure reconstruction and meets the brain science research of high-throughput neural circuit network reconstruction demand. |
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| Keywords: | brain project neuron circuit reconstruction machine learning deep learning image registration feature extraction |
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