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Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction [article]

Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, Hayit Greenspan
2021 arXiv   pre-print
Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution.  ...  Most approaches use discrete clustering which fails to capture the continuous nature of image rotation, others suffer from low-quality image reconstruction.  ...  We compare our approach and the spatial-VAE method, which is considered a leading method in learning rotation-invariant features for Cryo-EM datasets.  ... 
arXiv:2101.03549v1 fatcat:gqbfz62nczbxldcabsddqqwwye

Imaging and characterizing cells using tomography

Myan Do, Samuel A. Isaacson, Gerry McDermott, Mark A. Le Gros, Carolyn A. Larabell
2015 Archives of Biochemistry and Biophysics  
SXT has a very high specimen throughput compared to other high-resolution structure imaging modalities; for example, tomographic data for reconstructing an entire eukaryotic cell is acquired in a matter  ...  We can learn much about cell function by imaging and quantifying sub-cellular structures, especially if this is done non-destructively without altering said structures.  ...  For the best possible tomographic reconstruction of the specimen, in addition to the specimen remaining invariant throughout imaging, the data should be as complete as possible.  ... 
doi:10.1016/j.abb.2015.01.011 pmid:25602704 pmcid:PMC4506273 fatcat:t6ycfl4nl5gm7juuhxv4z7jkyi

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement [article]

Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye
2021 arXiv   pre-print
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference  ...  In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications.  ...  (b) spatial-VAE [43], disentangling translation/rotation features from different semantics.  ... 
arXiv:2105.08040v2 fatcat:56gnjk7y45a7jifx4s6npb6zxy

End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data [article]

Youssef S. G. Nashed, Frederic Poitevin, Harshit Gupta, Geoffrey Woollard, Michael Kagan, Chuck Yoon, Daniel Ratner
2021 arXiv   pre-print
Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations.  ...  Here, we present an end-to-end unsupervised approach that learns individual particle orientations from cryo-EM data while reconstructing the average 3D map of the biomolecule, starting from a random initialization  ...  ACKNOWLEDGMENTS We thank Wah Chiu, Khanh Dao Duc, Mark Hunter, TJ Lane, Julien Martel, Nina Miolane, and Gordon Wetzstein for numerous discussions that helped shape this project.  ... 
arXiv:2107.02958v1 fatcat:tdkzngji2zcsrogq3ykgrx6lb4

Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks [article]

Nina Miolane, Frédéric Poitevin, Yee-Ting Li, Susan Holmes
2021 arXiv   pre-print
Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution.  ...  In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images.  ...  Acknowledgments We thank Khanh Dao Duc and TJ Lane for very helpful comments on an earlier draft of the manuscript, and Daniel Ratner and Cornelius Gati for stimulating discussions and support throughout  ... 
arXiv:1911.08121v2 fatcat:l2snznfj6bgoznvkbotzyx4c6y

Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks

Eugene Palovcak, Daniel Asarnow, Melody G. Campbell, Zanlin Yu, Yifan Cheng
2020 IUCrJ  
In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline  ...  Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions  ...  Acknowledgements We thank Drs Joshua Batson, David Dynerman and David Agard for thoughtful discussions. The authors declare no competing interests related to this manuscript.  ... 
doi:10.1107/s2052252520013184 pmid:33209325 pmcid:PMC7642784 fatcat:kyyfxlfna5dddl6jx277hkcx6u

The Promise and the Challenges of Cryo‐Electron Tomography

Martin Turk, Wolfgang Baumeister
2020 FEBS Letters  
Cryo-electron tomography combines the power of three-dimensional molecular level imaging with the best structural preservation that is physically possible to achieve.  ...  Here we review state-of-the-art cryo-electron tomography workflows, provide examples of biological applications and discuss what is needed to realize the full potential of cryo-electron tomography.  ...  We are grateful to Miroslava Schaffer for the cryo-FIB lift-out image, and to Ben Engel and Qiang Guo for providing the in situ tomography images.  ... 
doi:10.1002/1873-3468.13948 pmid:33020915 fatcat:ufibivczfvh4bfys2mhe5hsifi

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4598227 fatcat:hm2ksetmsvf37adjjefmmbakvq

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4591029 fatcat:zn2hvfyupvdwlnvsscdgswayci

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4399748 fatcat:63ggmnviczg6vlnqugbnrexsgy

Computational Methods for Single-Particle Cryo-EM [article]

Amit Singer, Fred J. Sigworth
2020 arXiv   pre-print
Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic  ...  This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that  ...  Cryo-EM is transmission microscopy using electrons, which like X-rays allow angstromscale features to be detected.  ... 
arXiv:2003.13828v1 fatcat:sa4ifg3vhvhmreh3juxcciylla

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2

Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan, Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov
2019 npj Computational Materials  
To circumvent this problem, we developed a deep learning framework for dynamic STEM imaging that is trained to find the structures (defects) that break a crystal lattice periodicity and apply it for mapping  ...  Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature  ...  ACKNOWLEDGMENTS The work on microscopy and synthesis was supported by the U.S.  ... 
doi:10.1038/s41524-019-0152-9 fatcat:5krtkavwajekdpyodbj4n3eugq

A critical comparison between the two current methods of viewing frozen, live cells in the electron microscope: cryo-electron microscopic tomography versus "deep-etch" electron microscopy

John Heuser
2001 Biomedical Reviews  
Arguably, the greatest challenge facing electron microscopy (EM) today is to understand the basic structure or architecture of the cytoplasm, itself.  ...  Electron microscopy can help to answer these questions, if it can reach the level of resolving individual macromolecules in whole cells, and if it can reach this level of resolution without at the same  ...  anaglyphs" for publication, and Jennifer Scott for computer backup on every step of the production procedure.  ... 
doi:10.14748/bmr.v12.121 fatcat:jyjst2yfz5cb7mk35bbwcjzisi

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4415407 fatcat:6dejwzzpmfegnfuktrld6zgpiq

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
doi:10.5281/zenodo.4413249 fatcat:35qbhenysfhvza2roihx52afuy
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