Anatomy-aided deep learning for medical image segmentation: A review

Lu Liu*, Jelmer Maarten Wolterink, Christoph Brune, Raymond N J Veldhuis

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

18 Citations (Scopus)
125 Downloads (Pure)

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.
Original languageEnglish
Article number11TR01
JournalPhysics in medicine and biology
Volume66
Issue number11
DOIs
Publication statusPublished - 7 Jun 2021

Keywords

  • UT-Hybrid-D

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