Anatomy-aided deep learning for medical image segmentation: a review.

Journal: Physics in medicine and biology
Published Date:

Abstract

Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatomical information which is the crucial cue for manual segmentation. In this paper, we provide a review of anatomy-aided DL for medical image segmentation which covers systematically summarized anatomical information categories and corresponding representation methods. We address known and potentially solvable challenges in anatomy-aided DL and present a categorized methodology overview on using anatomical information with DL from over 70 papers. Finally, we discuss the strengths and limitations of the current anatomy-aided DL approaches and suggest potential future work.

Authors

  • Lu Liu
    College of Pharmacy, Harbin Medical University, Harbin, China.
  • Jelmer M Wolterink
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • Christoph Brune
  • Raymond N J Veldhuis