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Subcellular structure segmentation from cryo-electron tomograms via machine learning

Li Zhou, View ORCID ProfileChao Yang, Weiguo Gao, View ORCID ProfileTalita Perciano, Karen M. Davies, Nicholas K. Sauter
doi: https://doi.org/10.1101/2020.04.09.034025
Li Zhou
1School of Mathematical Sciences, Fudan University, Shanghai, China 200433
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Chao Yang
3Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
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  • For correspondence: CYang@lbl.gov
Weiguo Gao
1School of Mathematical Sciences, Fudan University, Shanghai, China 200433
2School of Data Sciences, Fudan University, Shanghai, China 200433
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Talita Perciano
3Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
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  • ORCID record for Talita Perciano
Karen M. Davies
4Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Nicholas K. Sauter
4Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Abstract

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify subcellular structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging techniques that try to piece together disconnected components that should belong to the same subcellular structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here work well for extracting membrane surfaces of protein reconstituted liposomes in a cellular environment that contains other artifacts.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 09, 2020.
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Subcellular structure segmentation from cryo-electron tomograms via machine learning
Li Zhou, Chao Yang, Weiguo Gao, Talita Perciano, Karen M. Davies, Nicholas K. Sauter
bioRxiv 2020.04.09.034025; doi: https://doi.org/10.1101/2020.04.09.034025
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Subcellular structure segmentation from cryo-electron tomograms via machine learning
Li Zhou, Chao Yang, Weiguo Gao, Talita Perciano, Karen M. Davies, Nicholas K. Sauter
bioRxiv 2020.04.09.034025; doi: https://doi.org/10.1101/2020.04.09.034025

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