scholarly journals Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Martijn T. Haring ◽  
Nalan Liv ◽  
A. Christiaan Zonnevylle ◽  
Angela C. Narvaez ◽  
Lenard M. Voortman ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Registration ◽  
Fluorescence And Electron Microscopy

scholarly journals VIPER: A Genetically-Encoded Tag for Fluorescence and Electron Microscopy

2019 ◽  
Vol 25 (S2) ◽  
pp. 1204-1205
Author(s):  
Julia K. Doh ◽  
Jonathan D. White ◽  
Hannah K. Zane ◽  
Young Hwan Chang ◽  
Claudia S. Lόpez ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Fluorescence And Electron Microscopy

scholarly journals Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images

2020 ◽  
Vol 10 (2) ◽  
pp. 86
Author(s):  
Chang Shu ◽  
Tong Xin ◽  
Fangxu Zhou ◽  
Xi Chen ◽  
Hua Han
Keyword(s):  
Electron Microscopy ◽  
Image Registration ◽  
Network Architecture ◽  
High Speed ◽  
Deformation Field ◽  
Volume Reconstruction ◽  
Microscopy Image ◽  
Complex Models ◽  
Field Estimation ◽  
Microscopy Images

It remains a mystery as to how neurons are connected and thereby enable use 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.

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