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

文献类型：期刊论文

作者Hao, Yu2,3; Wan, Xiaohua3; Yan, Rui3; Liu, Zhiyong3; Li, Jintao3; Zhang, Shihua2; Cui, Xuefeng1; Zhang, Fa3
刊名COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
出版日期2022-06-01
卷号221页码:14
ISSN号0169-2607
关键词Cryo-ET Sub-tomogram averaging Particle localization Particle classification Convolutional neural networks
DOI10.1016/j.cmpb.2022.106871
英文摘要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.
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
语种英语
出版者ELSEVIER IRELAND LTD
WOS记录号WOS:000856847800008
源URL[http://119.78.100.204/handle/2XEOYT63/19822]  
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.Shandong Univ, Sch Comp Sci & Technol, Qingdao, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
3.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, 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.

入库方式： OAI收割

来源：计算技术研究所

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