Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography

doi:10.1007/s11390-025-4816-2

	Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography
	Wang, Yao-Yu 1,2; Wan, Xiao-Hua 3; Chen, Cheng 4; Zhang, Fa 3; Cui, Xue-Feng 4
	2025-05-22
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
页码	13
摘要	Advances in cryo-electron tomography (cryo-ET) have enabled the visualization of molecules within their native cellular environments in three-dimensions (3D). These visualizations are essential for studying the functions of biological entities in their natural conditions. Recently, deep learning techniques have shown significant success in tackling the challenge of particle detection in cryo-ET data. However, accurately identifying and classifying multi-class molecules remain challenging due to factors like low signal-to-noise ratios and the wide range of particle sizes. In this study, we introduce a novel framework CFNPicker for 3D object detection applied to cryo-ET analysis. A major advantage of our method is the design of central feature network (CFN) to integrate central features across multiple scales, allowing for the accurate detection of both small (<= 200) and large (>= 600) molecules. Additionally, we propose an adaptive weighted sampling training strategy to distinguish the complex noise distribution in the background, reducing false positive particles. We also construct the localization label to explicitly utilize the size and position variations of multi-class protein structures. Compared with existing methods, CFN improves the F1 score for classification by 3.6%, 7.3%, 6.6%, and 5.1% for the four smallest molecules tested respectively, while preserving similar or higher F1 scores for other molecules analyzed.
关键词	particle detection cryo-electron tomography pattern recognition deep learning
DOI	10.1007/s11390-025-4816-2
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFF0704300] ; National Natural Science Foundation of China[32241027] ; National Natural Science Foundation of China[62072441] ; National Natural Science Foundation of China[62072283]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:001492846400001
出版者	SPRINGER SINGAPORE PTE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42404
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Fa; Cui, Xue-Feng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China 4.Shandong Univ, Sch Comp Sci & Technol, Qingdao 266237, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yao-Yu,Wan, Xiao-Hua,Chen, Cheng,et al. Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2025:13.
APA	Wang, Yao-Yu,Wan, Xiao-Hua,Chen, Cheng,Zhang, Fa,&Cui, Xue-Feng.(2025).Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,13.
MLA	Wang, Yao-Yu,et al."Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2025):13.