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.

近年来，深度学习（DL）已广泛用于医学图像分割。然而，尽管取得了这些进步，但仍然存在基于 DL 的分割失败的问题。最近，一些 DL 方法通过使用解剖信息取得了突破，这是手动分割的关键线索。在本文中，我们回顾了用于医学图像分割的解剖辅助深度学习，其中涵盖了系统总结的解剖信息类别和相应的表示方法。我们解决了解剖学辅助 DL 中已知和可能解决的挑战，并提供了一个分类的方法概述，该方法概述了使用来自 70 多篇论文的 DL 的解剖信息。最后，我们讨论了当前解剖辅助深度学习方法的优势和局限性，并提出了未来的潜在工作。