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Human attention-inspired volume reconstruction method on serial section electron microscopy images

文献类型：期刊论文

作者Zhou, Fangxu1,3; Shen, Lijun1; Chen, Bohao1; Chen, Xi1; Hua, Han1,2,3
刊名CYTOMETRY PART A
出版日期2021-03-18
页码11
关键词3D volume reconstruction electron microscopy image image registration
ISSN号1552-4922
DOI10.1002/cyto.a.24332
通讯作者Chen, Xi(xi.chen@ia.ac.cn) ; Hua, Han(hua.han@ia.ac.cn)
英文摘要The alignment of a 2D microscopic image stack to create a 3D image volume is an indispensable aspect of serial section electron microscopy (EM) technology, which could restore the original 3D integrity of biological tissues destroyed by chemical fixation and physical dissection. However, due to the similar texture intrasection and complex variations intersections of neural images, previous registration methods usually failed to yield reliable correspondences. And this also led to misalignment and impeded restoring the z-axis anatomical continuity of the neuron volume. In this article, inspired by human behaviors in finding correspondences, which use the topological relationship of image contents, we developed a spatial attention-based registration method for serial EM images to improve registration accuracy. Our approach combined the U-Net framework with spatial transformer networks (STN) to regress corresponding transformation maps in an unsupervised training fashion. The spatial attention (SA) module was incorporated into the U-Net architecture to increase the distinctiveness of image features by modeling its topological relationship. Experiments are conducted on both simulated and real data sets (MAS and RegCremi). Quantitative and qualitative comparisons demonstrate that our approach results in state of art accuracy (using the evaluation index of NCC, SSIM, Dice, Landmark error) and providing smooth and reliable transformation with less texture blur and unclear boundary than existing techniques. Our method is able to restore image stacks for visualization and quantitative analysis of EM image sequences.
资助项目Bureau of International Cooperation, Chinese Academy of Sciences[153D31KYSB20170059] ; National Natural Science Foundation of China[61673381] ; National Natural 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]
WOS研究方向Biochemistry & Molecular Biology ; Cell Biology
语种英语
WOS记录号WOS:000630082200001
出版者WILEY
资助机构Bureau of International Cooperation, Chinese Academy of Sciences ; National Natural Science Foundation of China ; Special Program of Beijing Municipal Science and Technology Commission ; Strategic Priority Research Program of Chinese Academy of Science
源URL[http://ir.ia.ac.cn/handle/173211/44076]  
专题类脑智能研究中心_微观重建与智能分析
通讯作者Chen, Xi; Hua, Han
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Fangxu,Shen, Lijun,Chen, Bohao,et al. Human attention-inspired volume reconstruction method on serial section electron microscopy images[J]. CYTOMETRY PART A,2021:11.
APA Zhou, Fangxu,Shen, Lijun,Chen, Bohao,Chen, Xi,&Hua, Han.(2021).Human attention-inspired volume reconstruction method on serial section electron microscopy images.CYTOMETRY PART A,11.
MLA Zhou, Fangxu,et al."Human attention-inspired volume reconstruction method on serial section electron microscopy images".CYTOMETRY PART A (2021):11.

入库方式： OAI收割

来源：自动化研究所

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