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Quantifying Variability of Manual Annotation in Cryo-Electron Tomograms

Published online by Cambridge University Press:  26 May 2016

Corey W. Hecksel
Affiliation:
Molecular Virology and Microbiology Department, Baylor College of Medicine, Houston, TX 77030, USA National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Michele C. Darrow
Affiliation:
Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Wei Dai
Affiliation:
National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Jesús G. Galaz-Montoya
Affiliation:
National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Jessica A. Chin
Affiliation:
National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Patrick G. Mitchell
Affiliation:
National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Shurui Chen
Affiliation:
National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Jemba Jakana
Affiliation:
National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Michael F. Schmid
Affiliation:
Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Wah Chiu
Affiliation:
Molecular Virology and Microbiology Department, Baylor College of Medicine, Houston, TX 77030, USA Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA National Center for Macromolecular Imaging, Baylor College of Medicine, Houston, TX 77030, USA
Corresponding
E-mail address:

Abstract

Although acknowledged to be variable and subjective, manual annotation of cryo-electron tomography data is commonly used to answer structural questions and to create a “ground truth” for evaluation of automated segmentation algorithms. Validation of such annotation is lacking, but is critical for understanding the reproducibility of manual annotations. Here, we used voxel-based similarity scores for a variety of specimens, ranging in complexity and segmented by several annotators, to quantify the variation among their annotations. In addition, we have identified procedures for merging annotations to reduce variability, thereby increasing the reliability of manual annotation. Based on our analyses, we find that it is necessary to combine multiple manual annotations to increase the confidence level for answering structural questions. We also make recommendations to guide algorithm development for automated annotation of features of interest.

Type
Technique and Instrumentation Development
Copyright
Copyright © Microscopy Society of America 2016

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Footnotes

Current address: Diamond Light Source Ltd, Science Division, Fermi Ave, Didcot, Oxfordshire OX11 0DX, UK.

Current address: Department of Cell Biology and Neuroscience, Center for Integrative Proteomics Research, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854-8076, USA.

a

Corey W. Hecksel and Michele C. Darrow contributed equally to this work.

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Hecksel supplementary material S1

Supplementary Table

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Hecksel supplementary material S2

Supplementary Table

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Hecksel supplementary material S3

Supplementary Movie

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Hecksel supplementary material S4

Supplementary Movie

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