Crowdsourcing image segmentation for deep learning: integrated platform for citizen science, paid microtask, and gamification.

Journal: Biomedizinische Technik. Biomedical engineering
PMID: 38143326

Abstract

OBJECTIVES: Segmentation is crucial in medical imaging. Deep learning based on convolutional neural networks showed promising results. However, the absence of large-scale datasets and a high degree of inter- and intra-observer variations pose a bottleneck. Crowdsourcing might be an alternative, as many non-experts provide references. We aim to compare different types of crowdsourcing for medical image segmentation.

Authors

  • Nicolai Spicher
    Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
  • Tim Wesemeyer
    Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony, Germany.
  • Thomas M Deserno
    Department of Medical Informatics, RWTH Aachen University, Pauwelsstr. 30, 52057 Aachen, Germany.

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