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Related Experiment Video

Updated: Mar 18, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

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Crowdsourcing for error detection in cortical surface delineations.

Melanie Ganz1, Daniel Kondermann2, Jonas Andrulis2

  • 1Neurobiology Research Unit, Center for Integrated Molecular Brain Imaging, Rigshospitalet, Juliane Maries Vej 28, 2100, Copenhagen, Denmark. melanie.ganz@nru.dk.

International Journal of Computer Assisted Radiology and Surgery
|June 29, 2016
PubMed
Summary
This summary is machine-generated.

Anonymous nonexperts can detect errors in automatic brain MRI analysis. Crowdsourcing quality control for neuroimaging data is feasible, improving efficiency and freeing up expert resources.

Keywords:
Cortical surfaceCrowdsourcingFreeSurferNeuroimaging

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Big Data in Healthcare

Background:

  • Neuroimaging datasets are rapidly expanding due to big data analysis trends.
  • Validating automatic processing of these datasets requires significant expert resources, posing a bottleneck.
  • Automated methods for neuroimaging analysis need efficient quality control mechanisms.

Purpose of the Study:

  • To evaluate the feasibility of using anonymous nonexperts from online communities for quality control of automated neuroimaging analysis.
  • To assess the capability of crowdsourcing for validating MR-based cortical surface delineations.
  • To determine if nonexpert input can effectively identify errors in automated neuroimaging data processing.

Main Methods:

  • Knowledge workers from an online crowdsourcing platform were engaged to annotate errors.
  • Annotations were performed on 100 central, coronal slices of MR images featuring automatic cortical surface delineations.
  • The study focused on quality control of MR-based cortical surface delineations.

Main Results:

  • Annotations for 100 images were completed in under an hour by the crowd.
  • The crowd achieved an average sensitivity of 82% and precision of 42% when compared to expert annotations.
  • Merging multiple crowd annotations per image increased sensitivity to 95% but reduced precision to 22%.

Conclusions:

  • Anonymous, untrained workers can feasibly detect errors in automated cortical surface delineations.
  • Crowdsourcing shows promise for quality control in neuroimaging analysis, potentially reducing reliance on expert time.
  • Future research aims to enhance crowd sensitivity for fully automated error detection, allowing experts to focus on correction.