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Crowdsourcing for error detection in cortical surface delineations

  • Original Article
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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

With the recent trend toward big data analysis, neuroimaging datasets have grown substantially in the past years. While larger datasets potentially offer important insights for medical research, one major bottleneck is the requirement for resources of medical experts needed to validate automatic processing results. To address this issue, the goal of this paper was to assess whether anonymous nonexperts from an online community can perform quality control of MR-based cortical surface delineations derived by an automatic algorithm.

Methods

So-called knowledge workers from an online crowdsourcing platform were asked to annotate errors in automatic cortical surface delineations on 100 central, coronal slices of MR images.

Results

On average, annotations for 100 images were obtained in less than an hour. When using expert annotations as reference, the crowd on average achieves a sensitivity of 82 % and a precision of 42 %. Merging multiple annotations per image significantly improves the sensitivity of the crowd (up to 95 %), but leads to a decrease in precision (as low as 22 %).

Conclusion

Our experiments show that the detection of errors in automatic cortical surface delineations generated by anonymous untrained workers is feasible. Future work will focus on increasing the sensitivity of our method further, such that the error detection tasks can be handled exclusively by the crowd and expert resources can be focused on error correction.

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Notes

  1. The instructions for the KWs can be seen here: http://melanie.clausundmelanie.de/projects/crowd.

  2. http://freesurfer.net.

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Acknowledgments

Collection of data that were included in the study was supported by the Lundbeck Foundation Center of Excellence Cimbi (R90-A7722). Melanie Ganz’ research was supported by the Carlsberg Foundation (2013-01-0502) and National Institutes of Health (NIH) (5R21EB018964-02). The work was further conducted within the setting of SFB TRR 125: cognition-guided surgery funded by the German Research Foundation (DFG) (Project A02).

Author information

Authors and Affiliations

  1. Neurobiology Research Unit, Center for Integrated Molecular Brain Imaging, Rigshospitalet, Juliane Maries Vej 28, 2100, Copenhagen, Denmark

    Melanie Ganz & Gitte Moos Knudsen

  2. Pallas Ludens GmbH, Im Bosseldorn 29, 69126, Heidelberg, Germany

    Daniel Kondermann & Jonas Andrulis

  3. Computer-assisted Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany

    Lena Maier-Hein

Authors
  1. Melanie Ganz
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  2. Daniel Kondermann
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  3. Jonas Andrulis
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  4. Gitte Moos Knudsen
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  5. Lena Maier-Hein
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Corresponding author

Correspondence to Melanie Ganz.

Ethics declarations

Conflict of interest

Melanie Ganz, Daniel Kondermann, Jonas Andrulis and Lena Maier-Hein declare that they have no conflict of interest. Gitte Moos Knudsen has been an invited lecturer at Pfizer A/S, worked as a consultant and received grants from H. Lundbeck A/S and is a stockholder of Novo Nordisk/Novozymes. Furthermore, she is on the board of directors of the BrainPrize and Elsass Foundation and the Advisory Board of the Kristian Jebsen Foundation and has authored for FADL and served as editor for Elsevier (IJNP).

Ethical standards

All procedures followed were in accordance with the ethical standards of the responsible committees on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study.

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Ganz, M., Kondermann, D., Andrulis, J. et al. Crowdsourcing for error detection in cortical surface delineations. Int J CARS 12, 161–166 (2017). https://doi.org/10.1007/s11548-016-1445-9

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  • DOI: https://doi.org/10.1007/s11548-016-1445-9

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