An Effective Microscopic Detection Method for Automated Silicon-Substrate Ultra-microtome (ASUM)

doi:10.1007/s11063-019-10134-5

	An Effective Microscopic Detection Method for Automated Silicon-Substrate Ultra-microtome (ASUM)
	Cheng, Long1,2 ; Liu, Weizhou1,2
发表期刊	NEURAL PROCESSING LETTERS
ISSN	1370-4621
	2019-11-11
页码	18
通讯作者	Cheng, Long(long.cheng@ia.ac.cn)
摘要	Three-dimensional (3D) representation of whole-brain cellular connectomics is the fundamental challenge for brain-inspired intelligence. And orderly automatic collection of brain sections on the silicon substrate is essential for the 3D imaging of cerebral ultrastructure. With the self-designed automated silicon-substrate ultra-microtome, serial brain sections can be orderly collected on the circular silicon substrates. In order to automate the collection process and further improve the efficiency of section collection, the form-invariant "Single Shot MultiBox-Detector" is proposed to detect the brain sections and baffles in the field of view of the microscope. And the "Cycle Generative Adversarial Networks" data augmentation method is proposed to alleviate the problem of fewer samples of the collected microscopic image dataset. The experimental results suggest that the proposed detection method could effectively detect the foreground objects in the microscopic images.
关键词	Microscopic object detection Deep learning Data augmentation Serial sections
DOI	10.1007/s11063-019-10134-5
关键词[WOS]	ELECTRON-MICROSCOPY ; CELL DETECTION ; BRAIN ; NEUROSCIENCE ; NETWORK
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[61633016] ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation[4162066] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[61633016] ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation[4162066]
项目资助者	National Natural Science Foundation of China ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000495736500001
出版者	SPRINGER
七大方向——子方向分类	智能机器人
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28913
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Cheng, Long
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Cheng, Long,Liu, Weizhou. An Effective Microscopic Detection Method for Automated Silicon-Substrate Ultra-microtome (ASUM)[J]. NEURAL PROCESSING LETTERS,2019:18.
APA	Cheng, Long,&Liu, Weizhou.(2019).An Effective Microscopic Detection Method for Automated Silicon-Substrate Ultra-microtome (ASUM).NEURAL PROCESSING LETTERS,18.
MLA	Cheng, Long,et al."An Effective Microscopic Detection Method for Automated Silicon-Substrate Ultra-microtome (ASUM)".NEURAL PROCESSING LETTERS (2019):18.