Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation method has far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. For the first time, from the perspective of full and imperfect annotation, we comprehensively compile 161 studies on deep learning-based multi-organ segmentation in multiple regions such as the head and neck, chest, and abdomen, containing a total of 214 related references. The method based on full annotation summarizes the existing methods from four aspects: network architecture, network dimension, network dedicated modules, and network loss function. The method based on imperfect annotation summarizes the existing methods from two aspects: weak annotation-based methods and semi annotation-based methods. We also summarize frequently used datasets for multi-organ segmentation and discuss new challenges and new research trends in this field.

翻译：从医学图像中精确分割头、颈、胸、腹部多个器官是计算机辅助诊断、手术导航和放射治疗的关键步骤。近年来，基于数据驱动的特征提取方法和端到端训练的深度学习自动多器官分割技术已显著超越传统方法，并成为新兴研究热点。本综述系统梳理了这一领域的最新进展，首次从完整标注与不完整标注两种视角，全面汇编了涵盖头颈、胸部、腹部等多区域共161项深度学习多器官分割研究（含214篇参考文献）。在完整标注方法部分，我们从网络架构、网络维度、专用模块及损失函数四个方面归纳现有技术；在不完整标注方法部分，则从弱标注和半标注两类策略进行总结。此外，我们归纳了多器官分割常用数据集，并探讨了该领域面临的新挑战与研究趋势。