Using diffusion models to generate synthetic labeled data for medical image segmentation.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jun 20, 2024

Abstract

PURPOSE: Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity.

Authors

  • Daniel G Saragih
    Department of Medical Imaging, University of Toronto, 263 McCaul Street, Toronto, M5T 1W7, ON, Canada.
  • Atsuhiro Hibi
    Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
  • Pascal N Tyrrell
    Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada. Electronic address: pascal.tyrrell@utoronto.ca.

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