More Web Proxy on the site http://driver.im/

Article

Accurate Detection of Proteins in Cryo-Electron Tomograms from Sparse Labels

Authors:

Alberto BartesaghiAuthors Info & Claims

Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI

Pages 644 - 660

https://doi.org/10.1007/978-3-031-19803-8_38

Published: 23 October 2022 Publication History

Abstract

Cryo-electron tomography (CET) combined with sub-volume averaging (SVA), is currently the only imaging technique capable of de-termining the structure of proteins imaged inside cells at molecular reso-lution. To obtain high-resolution reconstructions, sub-volumes containing randomly distributed copies of the protein of interest need be identified, extracted and subjected to SVA, making accurate particle detection a critical step in the CET processing pipeline. Classical template-based methods have high false-positive rates due to the very low signal-to-noise ratios (SNR) typical of CET volumes, while more recent neural-network based detection algorithms require extensive labeling, are very slow to train and can take days to run. To address these issues, we propose a novel particle detection framework that uses positive-unlabeled learning and exploits the unique properties of 3D tomograms to improve detec-tion performance. Our end-to-end framework is able to identify particles within minutes when trained using a single partially labeled tomogram. We conducted extensive validation experiments on two challenging CET datasets representing different experimental conditions, and observed more than improvement in mAP and F1 scores compared to existing particle picking methods used in CET. Ultimately, the proposed framework will facilitate the structural analysis of challenging biomedical targets imaged within the native environment of cells.

References

[1]

Al-Azzawi A, Ouadou A, Tanner JJ, and Cheng J Autocryopicker: an unsupervised learning approach for fully automated single particle picking in cryo-EM images BMC Bioinformatics 2019 20 1 1-26

[2]

Bendory T, Bartesaghi A, and Singer A Single-particle cryo-electron microscopy: mathematical theory, computational challenges, and opportunities IEEE Sig. Process. Mag. 2020 37 2 58-76

[3]

Bepler T, Morin A, Noble AJ, Brasch J, Shapiro L, and Berger B Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs Nat. Methods 2019 16 1-8

[4]

Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. arXiv abs/2006.10511 (2020)

[5]

Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. arXiv abs/2002.05709 (2020)

[6]

Chen W, Liu B, Peng S, Sun J, and Qiao X Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, and van Walsum T S3D-UNet: separable 3D U-net for brain tumor segmentation Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2019 Cham Springer 358-368

[7]

Chuang, C.Y., Robinson, J., Lin, Y.C., Torralba, A., Jegelka, S.: Debiased contrastive learning. arXiv (2020)

[8]

Doerr A Cryo-electron tomography Nat. Methods 2017 14 1 34-34

[9]

Druck, G., Mann, G.S., McCallum, A.: Learning from labeled features using generalized expectation criteria. In: SIGIR’08 (2008)

[10]

Eisenstein F, Danev R, and Pilhofer M Improved applicability and robustness of fast cryo-electron tomography data acquisition J. Struct. Biol. 2019 208 2 107-114

[11]

Gubins, I., et al.: SHREC 2021: Classification in cryo-electron tomograms (2021)

[12]

He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.B.: Momentum contrast for unsupervised visual representation learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9726–9735 (2020)

[13]

Huang, Q., Zhou, Y., Liu, H.F., Bartesaghi, A.: Weakly supervised learning for joint image denoising and protein localization in cryo-electron microscopy. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3260–3269 (2022)

[14]

Iudin A, Korir PK, Salavert-Torres J, Kleywegt GJ, and Patwardhan A EMPIAR: a public archive for raw electron microscopy image data Nat. Methods 2016 13 387-388

[15]

Jin Q, Meng ZP, Sun C, Wei L, and Su R RA-UNeT: A hybrid deep attention-aware network to extract liver and tumor in CT scans Front. Bioeng. Biotechnol. 2020 8 1471

[16]

Ke, Z., Wang, D., Yan, Q., Ren, J.S.J., Lau, R.W.H.: Dual student: Breaking the limits of the teacher in semi-supervised learning. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6727–6735 (2019)

[17]

Khosla, P., et al.: Supervised contrastive learning. arXiv abs/2004.11362 (2020)

[18]

Kiryo, R., Niu, G., du Plessis, M.C., Sugiyama, M.: Positive-unlabeled learning with non-negative risk estimator. arXiv (2017)

[19]

Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. arXiv abs/1808.01244 (2018)

[20]

Lin TY, Goyal P, Girshick RB, He K, and Dollár P Focal loss for dense object detection IEEE Trans. Pattern Anal. Mach. Intell. 2020 42 318-327

[21]

Liu W et al. Leibe B, Matas J, Sebe N, Welling M, et al. SSD: single shot multibox detector Computer Vision – ECCV 2016 2016 Cham Springer 21-37

[22]

Moebel, E., et al.: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nat. Methods (2021)

[23]

Nguyen NP, Ersoy I, Gotberg J, et al. DRPnet: automated particle picking in cryo-electron micrographs using deep regression BMC Bioinf. 2021 22 55

[24]

du Plessis, M.C., Niu, G., Sugiyama, M.: Analysis of learning from positive and unlabeled data. In: NIPS (2014)

[25]

Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

[26]

Ren S, He K, Girshick RB, and Sun J Faster r-cnn: Towards real-time object detection with region proposal networks IEEE Trans. Pattern Anal. Mach. Intell. 2015 39 1137-1149

[27]

Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., Pfister, T.: A simple semi-supervised learning framework for object detection. arXiv (2020)

[28]

Tang G et al. Eman2: an extensible image processing suite for electron microscopy J. Struct. Biol. 2007 157 1 38-46

[29]

Tegunov D, Xue L, Dienemann C, Cramer P, and Mahamid J Multi-particle cryo-em refinement with m visualizes ribosome-antibiotic complex at 3.5 å in cells Nat. Methods 2021 18 186-193

[30]

de Teresa, I., et al.: Convolutional networks for supervised mining of molecular patterns within cellular context. bioRxiv (2022)

[31]

Tian, Z., Shen, C., Chen, H., He, T.: Fcos: fully convolutional one-stage object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9626–9635 (2019)

[32]

Wagner T et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM Commun. Biol. 2019 2 1 218

[33]

Xu, M., et al.: End-to-end semi-supervised object detection with soft teacher. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3040–3049 (2021)

[34]

Zeng, X., Kahng, A., Xue, L., Mahamid, J., Chang, Y.W., Xu, M.: Disca: high-throughput cryo-et structural pattern mining by deep unsupervised clustering. bioRxiv (2021)

[35]

Zhao, X., et al.: Contrastive learning for label-efficient semantic segmentation. arXiv abs/2012.06985 (2020)

[36]

Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv abs/1904.07850 (2019)

Cited By

Martinez-Sanchez AHomberg UAlmira JPhelippeau H(2024)Tensorial Template Matching for Fast Cross-Correlation with Rotations and Its Application for TomographyComputer Vision – ECCV 202410.1007/978-3-031-73383-3_2(19-35)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73383-3_2
Zhao YBian HMu MUddin MLi ZLi XWang TXu M(2024)CryoSAM: Training-Free CryoET Tomogram Segmentation with Foundation ModelsMedical Image Computing and Computer Assisted Intervention – MICCAI 202410.1007/978-3-031-72111-3_12(124-134)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-72111-3_12

Index Terms

Accurate Detection of Proteins in Cryo-Electron Tomograms from Sparse Labels
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

Template-free detection of macromolecular complexes in cryo electron tomograms

Motivation: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this ...
A Divide and Conquer Algorithm for Electron Microscopy Segmentation
BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Cryo-Electron Microscopy is a biophysical technique able to visualize macromolecular complexes by producing 3-dimensional images. Currently, it has been advanced to be the second popular technique to construct protein molecules in terms of the number of ...
Using Combined Features to Analyze Atomic Structures derived from Cryo-EM Density Maps
BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. Many atomic structures have been derived from cryo-EM density maps of about 3Å resolution. Side-chain conformations are well determined in density maps ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI

Oct 2022

811 pages

ISBN:978-3-031-19802-1

DOI:10.1007/978-3-031-19803-8

Editors:
Shai Avidan
Tel Aviv University, Tel Aviv, Israel
,
Gabriel Brostow
University College London, London, UK
,
Moustapha Cissé
Google AI, Accra, Ghana
,
Giovanni Maria Farinella
University of Catania, Catania, Italy
,
Tal Hassner
Facebook (United States), Menlo Park, CA, USA

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 October 2022

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Martinez-Sanchez AHomberg UAlmira JPhelippeau H(2024)Tensorial Template Matching for Fast Cross-Correlation with Rotations and Its Application for TomographyComputer Vision – ECCV 202410.1007/978-3-031-73383-3_2(19-35)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73383-3_2
Zhao YBian HMu MUddin MLi ZLi XWang TXu M(2024)CryoSAM: Training-Free CryoET Tomogram Segmentation with Foundation ModelsMedical Image Computing and Computer Assisted Intervention – MICCAI 202410.1007/978-3-031-72111-3_12(124-134)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-72111-3_12

View Options

View options

Media

Figures

Other

Tables

View Table of Contents