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Sparse self-attention aggregation networks for neural sequence slice interpolation

文献类型：期刊论文

作者Wang,Zejin2,4; Liu,Jing2,4; Chen,Xi4; Li,Guoqing4; Han,Hua1,3,4; Chen, Xi; Han, Hua; Liu, Jing; Li, Guoqing; Wang, Zejin
刊名BioData Mining
出版日期2021-02-01
卷号14期号:1页码:19
关键词Slice interpolation Biological tissue recovery EM images Sparse self-attention network Adaptive style-balance loss
ISSN号1756-0381
DOI10.1186/s13040-021-00236-z
通讯作者Li,Guoqing(guoqing.li@ia.ac.cn) ; Han,Hua(hua.han@ia.ac.cn)
英文摘要AbstractBackgroundMicroscopic imaging is a crucial technology for visualizing neural and tissue structures. Large-area defects inevitably occur during the imaging process of electron microscope (EM) serial slices, which lead to reduced registration and semantic segmentation, and affect the accuracy of 3D reconstruction. The continuity of biological tissue among serial EM images makes it possible to recover missing tissues utilizing inter-slice interpolation. However, large deformation, noise, and blur among EM images remain the task challenging. Existing flow-based and kernel-based methods have to perform frame interpolation on images with little noise and low blur. They also cannot effectively deal with large deformations on EM images.ResultsIn this paper, we propose a sparse self-attention aggregation network to synthesize pixels following the continuity of biological tissue. First, we develop an attention-aware layer for consecutive EM images interpolation that implicitly adopts global perceptual deformation. Second, we present an adaptive style-balance loss taking the style differences of serial EM images such as blur and noise into consideration. Guided by the attention-aware module, adaptively synthesizing each pixel aggregated from the global domain further improves the performance of pixel synthesis. Quantitative and qualitative experiments show that the proposed method is superior to the state-of-the-art approaches.ConclusionsThe proposed method can be considered as an effective strategy to model the relationship between each pixel and other pixels from the global domain. This approach improves the algorithm’s robustness to noise and large deformation, and can accurately predict the effective information of the missing region, which will greatly promote the data analysis of neurobiological research.
资助项目NSFC[61701497] ; Instrument function development innovation program of Chinese Academy of Sciences[E0S92308] ; Instrument function development innovation program of Chinese Academy of Sciences[282019000057] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32030200]
WOS研究方向Mathematical & Computational Biology
语种英语
WOS记录号BMC:10.1186/S13040-021-00236-Z
出版者BioMed Central
资助机构NSFC ; Instrument function development innovation program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Science
源URL[http://ir.ia.ac.cn/handle/173211/42242]  
专题类脑智能研究中心_微观重建与智能分析
通讯作者Li,Guoqing; Han,Hua; Han, Hua; Li, Guoqing
作者单位1.School of Future Technology, University of Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences
4.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714		 Wang,Zejin,Liu,Jing,Chen,Xi,et al. Sparse self-attention aggregation networks for neural sequence slice interpolation[J]. BioData Mining,2021,14(1):19.
APA Wang,Zejin.,Liu,Jing.,Chen,Xi.,Li,Guoqing.,Han,Hua.,...&Wang, Zejin.(2021).Sparse self-attention aggregation networks for neural sequence slice interpolation.BioData Mining,14(1),19.
MLA Wang,Zejin,et al."Sparse self-attention aggregation networks for neural sequence slice interpolation".BioData Mining 14.1(2021):19.

入库方式： OAI收割

来源：自动化研究所

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