SHREC 2021: Classification in Cryo-electron Tomograms

Gubins, Ilja; Chaillet, Marten L.; Schot, Gijs van der; Trueba, M. Cristina; Veltkamp, Remco C.; Förster, Friedrich; Wang, Xiao; Kihara, Daisuke; Moebel, Emmanuel; Nguyen, Nguyen P.; White, Tommi; Bunyak, Filiz; Papoulias, Giorgos; Gerolymatos, Stavros; Zacharaki, Evangelia I.; Moustakas, Konstantinos; Zeng, Xiangrui; Liu, Sinuo; Xu, Min; Wang, Yaoyu; Chen, Cheng; Cui, Xuefeng; Zhang, Fa

SHREC 2021: Classification in Cryo-electron Tomograms

Files

005-017.pdf (3.37 MB)

Date

2021

Publisher

The Eurographics Association

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.

        @inproceedings{10.2312:3dor.20211307
,
booktitle = {Eurographics Workshop on 3D Object Retrieval
},
editor = {Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
},
title = {{SHREC 2021: Classification in Cryo-electron Tomograms
}},
author = {Gubins, Ilja and 
Chaillet, Marten L. and 
White, Tommi and 
Bunyak, Filiz and 
Papoulias, Giorgos and 
Gerolymatos, Stavros and 
Zacharaki, Evangelia I. and 
Moustakas, Konstantinos and 
Zeng, Xiangrui and 
Liu, Sinuo and 
Xu, Min and 
Wang, Yaoyu and 
Schot, Gijs van der and 
Chen, Cheng and 
Cui, Xuefeng and 
Zhang, Fa and 
Trueba, M. Cristina and 
Veltkamp, Remco C. and 
Förster, Friedrich and 
Wang, Xiao and 
Kihara, Daisuke and 
Moebel, Emmanuel and 
Nguyen, Nguyen P.
},
year = {2021
},
publisher = {The Eurographics Association
},
ISSN = {1997-0471
},
ISBN = {978-3-03868-137-3
},
DOI = {10.2312/3dor.20211307
}
}