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、胸腔及腹部的多个器官是计算机辅助诊断、手术导航及放射治疗的关键步骤。近年来，基于数据驱动特征提取与端到端训练的自动深度学习多器官分割方法已显著超越传统方法，成为新兴研究课题。本综述系统总结了该领域的最新研究进展。我们首次从完全标注与不完善标注的视角，全面整合了涵盖头颈、胸腔、腹部等多区域的161项深度学习多器官分割研究，共涉及214篇参考文献。基于完全标注的方法从网络架构、网络维度、专用模块及损失函数四个维度归纳现有工作；基于不完善标注的方法则从弱监督与半监督两个方向进行总结。此外，我们还梳理了多器官分割的常用数据集，并探讨了该领域的新挑战与研究趋势。