VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms

doi:10.1016/j.cmpb.2022.106871

	VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms
	Hao, Yu 1,2; Wan, Xiaohua 1; Yan, Rui 1; Liu, Zhiyong 1; Li, Jintao 1; Zhang, Shihua 2; Cui, Xuefeng 3; Zhang, Fa 1
	2022-06-01
发表期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
卷号	221 页码:14
摘要	Background and objective: Cryo-electron tomography (cryo-ET) with subtomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. Due to the low signal-to-noise ratio, the missing wedge artifacts in tomographic reconstructions, and multiple macromolecules of varied shapes and sizes, macromolecule localization and classification remain challenging. To tackle this bottleneck problem for structural determination by STA, we design an accurate macromolecule localization and classification method named voxelwise particle detector (VP-Detector). Methods: VP-Detector is a two-stage particle detection method based on a 3D multiscale dense convolutional neural network (3D MSDNet). The proposed network uses 3D hybrid dilated convolution (3D HDC) to avoid the resolution loss caused by scaling operations. Meanwhile, it uses 3D dense connectivity to encourage the reuse of feature maps to reduce trainable parameters. In addition, the weighted focal loss is proposed to focus more attention on difficult samples and rare classes, which relieves the class imbalance caused by multiple particles of various sizes. The performance of VP-Detector is evaluated on both simulated and real-world tomograms, and it shows that VP-Detector outperforms state-of-the-art methods. Results: The experiments show that VP-Detector outperforms the state-of-the-art methods on particle localization with an F1-score of 0.951 and a precision of 0.978. In addition, VP-Detector can replace manual particle picking in experiment on the real-world tomograms. Furthermore, it performs well in classifying large-, medium-, and small-weight proteins with accuracies of 1, 0.95, and 0.82, respectively. Finally, ablation studies demonstrate the effectiveness of 3D HDC, 3D dense connectivity, weighted focal loss, and training on small training sets. Conclusions: VP-Detector can achieve high accuracy in particle detection with few trainable parameters and support training on small datasets. It can also relieve the class imbalance caused by multiple particles with various shapes and sizes. (C) 2022 Elsevier B.V. All rights reserved.
关键词	Cryo-ET Sub-tomogram averaging Particle localization Particle classification Convolutional neural networks
DOI	10.1016/j.cmpb.2022.106871
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFF0704300] ; National Key Research and Development Program of China[2017YFA0504702] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA16021400] ; NSFC[61932018] ; NSFC[62072441] ; NSFC[62072280] ; NSFC[62072283]
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS记录号	WOS:000856847800008
出版者	ELSEVIER IRELAND LTD
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://119.78.100.204/handle/2XEOYT63/19823
专题	中国科学院计算技术研究所期刊论文
通讯作者	Zhang, Shihua; Cui, Xuefeng; Zhang, Fa
作者单位	1.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Shandong Univ, Sch Comp Sci & Technol, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Hao, Yu,Wan, Xiaohua,Yan, Rui,et al. VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2022,221:14.
APA	Hao, Yu.,Wan, Xiaohua.,Yan, Rui.,Liu, Zhiyong.,Li, Jintao.,...&Zhang, Fa.(2022).VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,221,14.
MLA	Hao, Yu,et al."VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 221(2022):14.