CASIA OpenIR  > 类脑智能研究中心  > 微观重建与智能分析
An unsupervised network for fast microscopic image registration
Shu, Chang1,2; Chen, Xi2; Xie, Qiwei2; Han, Hua2,3,4
2018
Conference NameSPIE Medical Imaging, 2018
Source PublicationMedical Imaging 2018: Digital Pathology
Volume10581
Pages105811D
Conference Date10-15 FEBRUARY, 2018
Conference PlaceHouston, Texas, United States
Abstract

At present, deep learning is widely used and has achieved excellent results in many fields except in the field of image registration, the reasons are two-fold: Firstly all the steps of deep learning should be derivable; nevertheless, the nonlinear deformation which is usually used in registration algorithms is hard to be depicted by explicit function. Secondly, success of deep learning is based on a large amount of labeled data, this is problematic for the application in real scenes. To address these concerns, we propose an unsupervised network for image registration. In order to integrate registration process into deep learning, image deformation is achieved by resampling, which can make deformation step derivable. The network optimizes its parameters directly by minimizing the loss between registered image and reference image without ground truth. To further improve algorithm's accuracy and speed, we incorporate coarse-to-fine multi-scale iterative scheme. We apply our method to register microscopic section images of neuron tissue. Compared with highly fine-tuning method sift flow, our method achieves similar accuracy with much less time.

KeywordMicroscopic image registration deep learning unsupervised learning coarse-to-fine multi-scale iterative scheme
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21723
Collection类脑智能研究中心_微观重建与智能分析
Corresponding AuthorHan, Hua
Affiliation1.University of Chinese Academy of Sciences, Beijing, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
4.The Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shu, Chang,Chen, Xi,Xie, Qiwei,et al. An unsupervised network for fast microscopic image registration[C],2018:105811D.
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