Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes
文献类型：期刊论文
| 作者 | Hong,Bei1,3 ; Liu,Jing1,3; Zhai,Hao1,3; Liu,Jiazheng1,3; Shen,Lijun1; Chen,Xi1; Xie,Qiwei4; Han,Hua1,2,3
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| 刊名 | BMC Bioinformatics
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| 出版日期 | 2022-10-31 |
| 卷号 | 23期号:1 |
| 关键词 | Connectomics Reconstruction Connectivity concept Joint optimization Electron microscope volumes |
| DOI | 10.1186/s12859-022-04991-6 |
| 通讯作者 | Shen,Lijun(lijun.shen@ia.ac.cn) ; Han,Hua(hua.han@ia.ac.cn) |
| 英文摘要 | AbstractBackgroundNanoscale 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.ResultsIn 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.ConclusionsWe 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. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S12859-022-04991-6 |
| 出版者 | BioMed Central |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50116]  |
| 专题 | 类脑智能研究中心_微观重建与智能分析 |
| 通讯作者 | Shen,Lijun; Han,Hua |
| 作者单位 | 1.Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation 2.CAS Center for Excellence in Brain Science and Intelligence Technology 3.University of Chinese Academy of Sciences; School of Artificial Intelligence, School of Future Technology 4.Beijing University of Technology; Research Base of Beijing Modern Manufacturing Development |
| 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). |
| 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). |
| MLA | Hong,Bei,et al."Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes".BMC Bioinformatics 23.1(2022). |
入库方式： OAI收割
来源：自动化研究所
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