Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.

Journal: Journal of digital imaging
Published Date: Aug 1, 2019

Abstract

Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

Authors

  • Mohammad Hesam Hesamian
    School of Electrical and Data Engineering (SEDE), University of Technology Sydney, 2007, Sydney, Australia. mh.hesamian@gmail.com.
  • Wenjing Jia
    School of Electrical and Data Engineering (SEDE), University of Technology Sydney, 2007, Sydney, Australia.
  • Xiangjian He
    School of Electrical and Data Engineering (SEDE), University of Technology Sydney, 2007, Sydney, Australia.
  • Paul Kennedy
    School of Software, University of Technology Sydney, 2007, Sydney, Australia.

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