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神经元形态重建工具的研究进展
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  • 英文篇名:An Overview of Advances of Tools in Neuron Reconstruction
  • 作者:李诗玮 ; 全廷伟 ; 周航 ; 李安安 ; 付玲 ; 龚辉 ; 骆清铭 ; 曾绍群
  • 英文作者:LI Shi-Wei;QUAN Ting-Wei;ZHOU Hang;LI An-An;FU Ling;GONG Hui;LUO Qing-Ming;ZENG Shao-Qun;Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology;MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences,Huazhong University of Science and Technology;School of Mathematics and Economics, Hubei University of Education;
  • 关键词:神经元形态 ; 高通量 ; 神经群落 ; 全脑尺度 ; 神经元重建
  • 英文关键词:neuronal morphology;;high throughput;;neuronal population;;brain-wide scale;;neuron reconstruction
  • 中文刊名:SHSW
  • 英文刊名:Progress in Biochemistry and Biophysics
  • 机构:华中科技大学-武汉光电国家研究中心Britton Chance生物医学光子学研究中心;华中科技大学工程科学学院生物医学光子学教育部重点实验室生物医学工程协同创新中心;湖北第二师范学院数学与经济学院;
  • 出版日期:2019-03-27 14:57
  • 出版单位:生物化学与生物物理进展
  • 年:2019
  • 期:v.46
  • 基金:国家自然科学基金委员会创新研究群体科学基金(61721092);; 国家自然科学基金(81327802,81771913);; 国家重点基础研究发展计划(973)(2015CB7556003);; 湖北省中青年创新研究群体科学基金(T201520);; 武汉光电国家研究中心主任基金(Wuhan National Laboratory for Optoelectronics,WNLO)资助项目~~
  • 语种:中文;
  • 页:SHSW201903005
  • 页数:10
  • CN:03
  • ISSN:11-2161/Q
  • 分类号:51-60
摘要
近年来,分子标记和显微光学成像技术的系列突破,使得单细胞分辨的全脑尺度神经群落成像成为现实.然而,现有神经元形态重建工具的发展速度远远滞后于海量数据的产生速度,难以满足现阶段成像数据的分析需求.在此背景下,我们首先分析了神经元形态重建工具发展滞后的原因,简述现有半自动和全自动神经元形态重建工具的特点和最新发展,并结合现有工具的特点分析其向高通量、高准确度重建工具发展时面临的挑战.最后,我们对未来形态重建工具的发展趋势及应用前景做出展望.
        Recent breakthroughs in molecular labeling and light microscopy enable the imaging of brain-wide neuronal population at cellular level. However, the development of neuronal reconstruction tools lags far behind massive datasets generation, which fails to meet the data analysis requirement at the current stage. In this sense,we first sought reasons for backwardness in current reconstruction tools and we summarized the features and introduced latest developments in these semi-automatic and fully automatic tools. Based on the summarized features, we further listed the challenges in developing reconstruction tools with high throughput and accuracy. In the end, we aired our views on the future development and application prospect of tools in neuron reconstruction.
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