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

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

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

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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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