Knowledge Commons of Institute of Automation,CAS
Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images | |
Shu, Chang1,2; Xin, Tong1,2; Zhou, Fangxu1,3; Chen, Xi1; Han, Hua3,4,5 | |
发表期刊 | Brain Sciences |
2020 | |
卷号 | 10期号:2页码:86 |
通讯作者 | Chen, Xi(xi.chen@ia.ac.cn) ; Han, Hua(hua.han@ia.ac.cn) |
摘要 | It remains a mystery as to how neurons are connected and thereby enable us to think, and volume reconstruction from series of microscopy sections of brains is a vital technique in determining this connectivity. Image registration is a key component; the aim of image registration is to estimate the deformation field between two images. Current methods choose to directly regress the deformation field; however, this task is very challenging. It is common to trade off computational complexity with precision when designing complex models for deformation field estimation. This approach is very inefficient, leading to a long inference time. In this paper, we suggest that complex models are not necessary and solve this dilemma by proposing a dual-network architecture. We divide the deformation field prediction problem into two relatively simple subproblems and solve each of them on one branch of the proposed dual network. The two subproblems have completely opposite properties, and we fully utilize these properties to simplify the design of the dual network. These simple architectures enable high-speed image registration. The two branches are able to work together and make up for each other's drawbacks, and no loss of accuracy occurs even when simple architectures are involved. Furthermore, we introduce a series of loss functions to enable the joint training of the two networks in an unsupervised manner without introducing costly manual annotations. The experimental results reveal that our method outperforms state-of-the-art methods in fly brain electron microscopy image registration tasks, and further ablation studies enable us to obtain a comprehensive understanding of each component of our network. |
关键词 | Computer Vision Image Processing Deep Learning Image Registration Electron Microscopy Image Dual Network Architecture Unsupervised Learning |
DOI | 10.3390/brainsci10020086 |
关键词[WOS] | RECONSTRUCTION ; VOLUME |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Science Foundation of China[61673381] ; National Science Foundation of China[61701497] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32030200] ; Instrument function development innovation program of Chinese Academy of Sciences[282019000057] ; Bureau of International Cooperation, CAS[153D31KYSB20170059] |
项目资助者 | National Science Foundation of China ; Special Program of Beijing Municipal Science and Technology Commission ; Strategic Priority Research Program of Chinese Academy of Science ; Instrument function development innovation program of Chinese Academy of Sciences ; Bureau of International Cooperation, CAS |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neurosciences |
WOS记录号 | WOS:000519243400035 |
出版者 | Brain Sciences |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38525 |
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 | Chen, Xi; Han, Hua |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China 4.The Center for Excellence in Brain Science and Intelligence Technology, CAS, Shanghai 200031, China 5.National Laboratory of Pattern Recognition, CASIA, Beijing 100190, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shu, Chang,Xin, Tong,Zhou, Fangxu,et al. Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images[J]. Brain Sciences,2020,10(2):86. |
APA | Shu, Chang,Xin, Tong,Zhou, Fangxu,Chen, Xi,&Han, Hua.(2020).Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images.Brain Sciences,10(2),86. |
MLA | Shu, Chang,et al."Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images".Brain Sciences 10.2(2020):86. |
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 | ||
Chang - 2020 - Dual (1388KB) | 期刊 | 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