Automatic particle picking and multi-class classification in cryo-electron tomograms

Xuanli Chen; Yuxiang Chen; Jan Michael Schuller; Nassir Navab; Friedrich Förster

doi:10.1109/isbi.2014.6868001

Automatic particle picking and multi-class classification in cryo-electron tomograms

Xuanli Chen, Yuxiang Chen, Jan Michael Schuller, Nassir Navab, Friedrich Förster

Informatics 16 - Chair of Computer Aided Medical Procedures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Macromolecular structure determination using cryo-electron tomography requires large amount of subtomograms depicting the same molecule, which are averaged. In this paper, we propose a novel automatic particle picking and classification method for cryo-electron tomograms. The workflow comprises two stages: detection and classification. The detection method consists of a template-free picking procedure based on anisotropic diffusion filtering and connected component analysis. For classification, a novel 3D rotation invariant feature descriptor named Sphere Ring Haar and a hierarchical classification algorithm consisting of two machine learning models (DBSCAN and random forest) are proposed. The performance of our method is superior compared to template matching based methods and we achieved over 90% true positive rates for detection of proteasomes and ribosomes in experimental data.

Original language	English
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	838-841
Number of pages	4
ISBN (Electronic)	9781467319591
DOIs	https://doi.org/10.1109/isbi.2014.6868001
State	Published - 29 Jul 2014
Event	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: 29 Apr 2014 → 2 May 2014

Publication series

Name	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Conference

Conference	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	29/04/14 → 2/05/14

Keywords

Automatic particle picking
Machine learning
Proteasome
Ribosome
Sphere ring haar

Access to Document

10.1109/isbi.2014.6868001

Cite this

Chen, X., Chen, Y., Schuller, J. M., Navab, N., & Förster, F. (2014). Automatic particle picking and multi-class classification in cryo-electron tomograms. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 838-841). Article 6868001 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6868001

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title = "Automatic particle picking and multi-class classification in cryo-electron tomograms",

abstract = "Macromolecular structure determination using cryo-electron tomography requires large amount of subtomograms depicting the same molecule, which are averaged. In this paper, we propose a novel automatic particle picking and classification method for cryo-electron tomograms. The workflow comprises two stages: detection and classification. The detection method consists of a template-free picking procedure based on anisotropic diffusion filtering and connected component analysis. For classification, a novel 3D rotation invariant feature descriptor named Sphere Ring Haar and a hierarchical classification algorithm consisting of two machine learning models (DBSCAN and random forest) are proposed. The performance of our method is superior compared to template matching based methods and we achieved over 90% true positive rates for detection of proteasomes and ribosomes in experimental data.",

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author = "Xuanli Chen and Yuxiang Chen and Schuller, {Jan Michael} and Nassir Navab and Friedrich F{\"o}rster",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",

year = "2014",

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doi = "10.1109/isbi.2014.6868001",

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series = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",

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Chen, X, Chen, Y, Schuller, JM, Navab, N & Förster, F 2014, Automatic particle picking and multi-class classification in cryo-electron tomograms. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6868001, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 838-841, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 29/04/14. https://doi.org/10.1109/isbi.2014.6868001

Automatic particle picking and multi-class classification in cryo-electron tomograms. / Chen, Xuanli; Chen, Yuxiang; Schuller, Jan Michael et al.
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 838-841 6868001 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic particle picking and multi-class classification in cryo-electron tomograms

AU - Chen, Xuanli

AU - Chen, Yuxiang

AU - Schuller, Jan Michael

AU - Navab, Nassir

AU - Förster, Friedrich

PY - 2014/7/29

Y1 - 2014/7/29

N2 - Macromolecular structure determination using cryo-electron tomography requires large amount of subtomograms depicting the same molecule, which are averaged. In this paper, we propose a novel automatic particle picking and classification method for cryo-electron tomograms. The workflow comprises two stages: detection and classification. The detection method consists of a template-free picking procedure based on anisotropic diffusion filtering and connected component analysis. For classification, a novel 3D rotation invariant feature descriptor named Sphere Ring Haar and a hierarchical classification algorithm consisting of two machine learning models (DBSCAN and random forest) are proposed. The performance of our method is superior compared to template matching based methods and we achieved over 90% true positive rates for detection of proteasomes and ribosomes in experimental data.

AB - Macromolecular structure determination using cryo-electron tomography requires large amount of subtomograms depicting the same molecule, which are averaged. In this paper, we propose a novel automatic particle picking and classification method for cryo-electron tomograms. The workflow comprises two stages: detection and classification. The detection method consists of a template-free picking procedure based on anisotropic diffusion filtering and connected component analysis. For classification, a novel 3D rotation invariant feature descriptor named Sphere Ring Haar and a hierarchical classification algorithm consisting of two machine learning models (DBSCAN and random forest) are proposed. The performance of our method is superior compared to template matching based methods and we achieved over 90% true positive rates for detection of proteasomes and ribosomes in experimental data.

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KW - Machine learning

KW - Proteasome

KW - Ribosome

KW - Sphere ring haar

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Chen X, Chen Y, Schuller JM, Navab N, Förster F. Automatic particle picking and multi-class classification in cryo-electron tomograms. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 838-841. 6868001. (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). doi: 10.1109/isbi.2014.6868001

Automatic particle picking and multi-class classification in cryo-electron tomograms

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Keywords

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