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

doi:10.1007/s11063-019-10134-5

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	An Effective Microscopic Detection Method for Automated Silicon-Substrate Ultra-microtome (ASUM)
	Cheng, Long 1,2; Liu, Weizhou 1,2
刊名	NEURAL PROCESSING LETTERS
	2019-11-11
页码	18
关键词	Microscopic object detection Deep learning Data augmentation Serial sections
ISSN号	1370-4621
DOI	10.1007/s11063-019-10134-5
通讯作者	Cheng, Long(long.cheng@ia.ac.cn)
英文摘要	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.
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]
WOS关键词	ELECTRON-MICROSCOPY ; CELL DETECTION ; BRAIN ; NEUROSCIENCE ; NETWORK
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000495736500001
资助机构	National Natural Science Foundation of China ; Research Fund for Young Top-Notch Talent of National Ten Thousand Talent Program ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28912]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
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
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.