Knowledge Commons of Institute of Automation,CAS
Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes | |
Hong,Bei1,2![]() ![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | BMC Bioinformatics
![]() |
ISSN | 1471-2105 |
2022-10-31 | |
卷号 | 23期号:1页码:23 |
摘要 | Abstract Background: Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. Results: In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. Conclusions: We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis. |
关键词 | Connectomics Reconstruction Connectivity concept Joint optimization Electron microscope volumes |
DOI | 10.1186/s12859-022-04991-6 |
关键词[WOS] | IMAGE ; SYSTEM |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Science and Technology Innovation 2030 Major Program[2021ZD0204503] ; National Science and Technology Innovation 2030 Major Program[2021ZD0204500] ; National Natural Science Foundation of China[32171461] ; National Natural Science Foundation of China[61673381] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030208] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA16021104] ; Program of Beijing Municipal Science & Technology Commission[Z201100008420004] |
项目资助者 | National Science and Technology Innovation 2030 Major Program ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Program of Beijing Municipal Science & Technology Commission |
WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
WOS类目 | Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
WOS记录号 | BMC:10.1186/s12859-022-04991-6 |
出版者 | BioMed Central |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | 其他 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50117 |
专题 | 脑图谱与类脑智能实验室_微观重建与智能分析 |
通讯作者 | Shen,Lijun; Han,Hua |
作者单位 | 1.University of Chinese Academy of Sciences; School of Artificial Intelligence, School of Future Technology 2.Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation 3.Beijing University of Technology; Research Base of Beijing Modern Manufacturing Development 4.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Hong,Bei,Liu,Jing,Zhai,Hao,et al. Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes[J]. BMC Bioinformatics,2022,23(1):23. |
APA | Hong,Bei.,Liu,Jing.,Zhai,Hao.,Liu,Jiazheng.,Shen,Lijun.,...&Han,Hua.(2022).Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes.BMC Bioinformatics,23(1),23. |
MLA | Hong,Bei,et al."Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes".BMC Bioinformatics 23.1(2022):23. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Joint reconstruction(3091KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论