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 towards 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.
Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Max Planck Institute for Biochemistry（马克斯·普朗克生物化学研究所）；Computational Biology Department, Carnegie Mellon University（卡内基梅隆大学计算生物学系）
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
University of Vienna（维也纳大学）；Research Institute of Molecular Pathology (IMP)（分子病理学研究所）；Brno University of Technology（布尔诺理工大学）；Institute of Science and Technology Austria (ISTA)（奥地利科学技术研究所）；Austrian Academy of Sciences（奥地利科学院）
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）；Nanjing University（南京大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California - Irvine（加州大学欧文分校）
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching
Carnegie Mellon University（卡内基梅隆大学）；University of Florida（佛罗里达大学）
