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Improved deep learning-based macromolecules structure classification from electron cryo-tomograms

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Abstract

Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macromolecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented.

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Acknowledgements

This work was supported in part by U.S. National Institutes of Health (NIH) Grant P41 GM103712. John Galeotti acknowledges support from NIH R01 Grant 1R01EY021641, National Library of Medicine contract HHSN27620100058OP and DoD Peer Reviewed Medical Research Program (PR130773, HRPO Log No. A-18237). Min Xu acknowledge support of Samuel and Emma Winters Foundation.

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Authors and Affiliations

  1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Chengqian Che & John Galeotti

  2. Department of Automation, Tsinghua University, Beijing, China

    Ruogu Lin

  3. Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA

    Xiangrui Zeng & Min Xu

  4. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA

    Karim Elmaaroufi

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  1. Chengqian Che
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  5. John Galeotti
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  6. Min Xu
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Correspondence to John Galeotti or Min Xu.

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Che, C., Lin, R., Zeng, X. et al. Improved deep learning-based macromolecules structure classification from electron cryo-tomograms. Machine Vision and Applications 29, 1227–1236 (2018). https://doi.org/10.1007/s00138-018-0949-4

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  • DOI: https://doi.org/10.1007/s00138-018-0949-4

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