Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells. For systematic recognition and recovery of macromolecular structures captured by CECT, methods for several important tasks such as subtomogram classification and semantic segmentation have been developed. However, the recognition and recovery of macromolecular structures are still very difficult due to high molecular structural diversity, crowding molecular environment, and the imaging limitations of CECT. In this paper, we propose a novel multi-task 3D convolutional neural network model for simultaneous classification, segmentation, and coarse structural recovery of macromolecules of interest in subtomograms. In our model, the learned image features of one task are shared and thereby mutually reinforce the learning of other tasks. Evaluated on realistically simulated and experimental CECT data, our multi-task learning model outperformed all single-task learning methods for classification and segmentation. In addition, we demonstrate that our model can generalize to discover, segment and recover novel structures that do not exist in the training data.
Disentangling semantic features of macromolecules in Cryo-Electron Tomography
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Xi’an Jiaotong University（西安交通大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）
Unsupervised SE(3) Disentanglement for in situ Macromolecular Morphology Identification from Cryo-Electron Tomography
Carnegie Mellon University（卡内基梅隆大学）；Indian Institute of Technology (IIT)（印度理工学院）；Division of CryoEM and Bioimaging SSRL SLAC National Accelerator Laboratory Stanford University（冷冻电镜与生物成像部SLAC国家加速器实验室斯坦福大学）
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；Harvard University（哈佛大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Liverpool（利物浦大学）
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究中心）；Leiden University（莱顿大学）；Thermo Fisher Scientific（赛默飞世尔科技）；Leiden Institute of Advanced Computer Science（莱顿高级计算机科学研究院）
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）
SHREC 2021: Classification in cryo-electron tomograms
Utrecht University（乌得勒支大学）；Purdue University（普渡大学）；Inria Rennes Bretagne Atlantique（法国国家信息与自动化研究所雷恩-布列塔尼大西洋分所）；University of Missouri（密苏里大学）；University of Patras（帕特雷大学）；Carnegie Mellon University（卡内基梅隆大学）；Shandong University（山东大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification
Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；National Institute of Technology Karnataka（卡纳塔克邦国立理工学院）；University of Minnesota – Twin Cities（明尼苏达大学双城分校）
Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo Labeling
School of Computing and Information Technology, University of Wollongong, Australia（澳大利亚卧龙岗大学计算与信息技术学院）；Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, USA（美国卡内基梅隆大学莱恩计算生物学系）；Centre for Nutrition and Food Sciences, The University of Queensland, Australia（澳大利亚昆士兰大学营养与食品科学中心）；School of Electrical and Information Engineering, University of Sydney, Australia（澳大利亚悉尼大学电气与信息工程学院）；Department of Business Strategy and Innovation, Griffith University, Australia（澳大利亚格里菲斯大学商业战略与创新系）；Department of Biological Sciences, Purdue University, USA（美国普渡大学生物科学系）；NVIDIA, USA（美国英伟达公司）
Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques
University of Dhaka（达卡大学）；Old Dominion University（欧道明大学）
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）
