Next Article in Journal
Synergistic Effects of Scalp Acupuncture and Repetitive Transcranial Magnetic Stimulation on Cerebral Infarction: A Randomized Controlled Pilot Trial
Next Article in Special Issue
Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures
Previous Article in Journal
Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression
Previous Article in Special Issue
Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States
Open AccessArticle

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

by Chang Shu 1,2, Tong Xin 1,2, Fangxu Zhou 1,3, Xi Chen 1,* and Hua Han 3,4,5,*
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
*
Authors to whom correspondence should be addressed.
Brain Sci. 2020, 10(2), 86; https://doi.org/10.3390/brainsci10020086
Received: 14 January 2020 / Accepted: 4 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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. View Full-Text
Keywords: computer vision; image processing; deep learning; image registration; electron microscopy image; dual network architecture; unsupervised learning computer vision; image processing; deep learning; image registration; electron microscopy image; dual network architecture; unsupervised learning
Show Figures

Figure 1

MDPI and ACS Style

Shu, C.; Xin, T.; Zhou, F.; Chen, X.; Han, H. Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images. Brain Sci. 2020, 10, 86.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop