SHREC 2021: Classification in cryo-electron tomograms

• URL: http://arxiv.org/abs/2203.10035v1
• Date: Fri, 18 Mar 2022 16:08:22 GMT
• Title: SHREC 2021: Classification in cryo-electron tomograms
• Authors: Ilja Gubins, Marten L. Chaillet, Gijs van der Schot, M. Cristina Trueba, Remco C. Veltkamp, Friedrich F\"orster, Xiao Wang, Daisuke Kihara, Emmanuel Moebel, Nguyen P. Nguyen, Tommi White, Filiz Bunyak, Giorgos Papoulias, Stavros Gerolymatos, Evangelia I. Zacharaki, Konstantinos Moustakas, Xiangrui Zeng, Sinuo Liu, Min Xu, Yaoyu Wang, Cheng Chen, Xuefeng Cui, Fa Zhang
• Abstract summary: cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. We generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms.
• Score: 13.443446070180562
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyze cryo-electron tomograms. To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryo-ET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM. We also experimentally confirm that there is a negative relationship between particle size and performance for all methods.

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