CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images

As an emerging technology, cryo-electron microscopy (cryo-EM) has attracted more and more research interests from both structural biology and computer science, because many challenging computational tasks are involved in the processing of cryo-EM images. An important image processing step is to clus...

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Bibliographic Details
Published in:Frontiers in genetics Vol. 11; p. 627746
Main Authors: Lei, Houchao, Yang, Yang
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 20.01.2021
Subjects:
autoencoder
clustering
cryo-EM
deep learning
Genetics
image denoising
deep learning
clustering
autoencoder
cryo-EM
image denoising
ISSN:1664-8021, 1664-8021
Online Access:Get full text
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Summary:As an emerging technology, cryo-electron microscopy (cryo-EM) has attracted more and more research interests from both structural biology and computer science, because many challenging computational tasks are involved in the processing of cryo-EM images. An important image processing step is to cluster the 2D cryo-EM images according to their projection angles, then the cluster mean images are used for the subsequent 3D reconstruction. However, cryo-EM images are quite noisy and denoising them is not easy, because the noise is a complicated mixture from samples and hardware. In this study, we design an effective cryo-EM image denoising model, CDAE, i.e., a cascade of denoising autoencoders. The new model comprises stacked blocks of deep neural networks to reduce noise in a progressive manner. Each block contains a convolutional autoencoder, pre-trained by simulated data of different SNRs and fine-tuned by target data set. We assess this new model on three simulated test sets and a real data set. CDAE achieves very competitive PSNR (peak signal-to-noise ratio) in the comparison of the state-of-the-art image denoising methods. Moreover, the denoised images have significantly enhanced clustering results compared to original image features or high-level abstraction features obtained by other deep neural networks. Both quantitative and visualized results demonstrate the good performance of CDAE for the noise reduction in clustering single-particle cryo-EM images.
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This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Reviewed by: Qi Zhao, University of Science and Technology Liaoning, China; Xiangxiang Zeng, Hunan University, China; Fei Guo, Tianjin University, China
Edited by: Lin Hua, Capital Medical University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2020.627746