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.
Original language | English |
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Title of host publication | EG 3DOR 2021 - Eurographics Workshop on 3D Object Retrieval Short Papers |
Editors | Dieter W. Fellner, Werner Hansmann, Werner Purgathofer, Francois Sillion |
Publisher | Eurographics Association |
Pages | 5-17 |
Number of pages | 13 |
ISBN (Electronic) | 9783038681373 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 Eurographics Workshop on 3D Object Retrieval, EG 3DOR 2021 - Virtual, Online Duration: 2 Sept 2021 → 3 Sept 2021 |
Publication series
Name | Eurographics Workshop on 3D Object Retrieval, EG 3DOR |
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Volume | 2021-September |
ISSN (Print) | 1997-0463 |
ISSN (Electronic) | 1997-0471 |
Conference
Conference | 2021 Eurographics Workshop on 3D Object Retrieval, EG 3DOR 2021 |
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City | Virtual, Online |
Period | 2/09/21 → 3/09/21 |
Bibliographical note
Funding Information:We want to thank all track participants for their contribution. This work was supported by the European Research Council under the European Union's Horizon2020 Programme (ERC Consolidator Grant Agreement 724425 - BENDER) and the Nederlandse Organisatie voor Wetenschappelijke Onderzoek (Vici 724.016.001 and 741.018.201).
Funding Information:
We want to thank all track participants for their contribution. This work was supported by the European Research Council under the European Union’s Horizon2020 Programme (ERC Consolidator Grant Agreement 724425 - BENDER) and the Nederlandse Organ-isatie voor Wetenschappelijke Onderzoek (Vici 724.016.001 and 741.018.201).
Publisher Copyright:
© 2021 The Author(s) Eurographics Proceedings © 2021 The Eurographics Association.
Funding
We want to thank all track participants for their contribution. This work was supported by the European Research Council under the European Union's Horizon2020 Programme (ERC Consolidator Grant Agreement 724425 - BENDER) and the Nederlandse Organisatie voor Wetenschappelijke Onderzoek (Vici 724.016.001 and 741.018.201). We want to thank all track participants for their contribution. This work was supported by the European Research Council under the European Union’s Horizon2020 Programme (ERC Consolidator Grant Agreement 724425 - BENDER) and the Nederlandse Organ-isatie voor Wetenschappelijke Onderzoek (Vici 724.016.001 and 741.018.201).
Keywords
- Evaluation of retrieval results
- Multimedia and multimodal retrieval
- Retrieval models and ranking
- Specialized information retrieval