Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

翻译：从医学图像中精确勾勒人体多个器官或异常区域，在计算机辅助诊断、手术仿真、图像引导介入治疗，尤其是放射治疗计划中具有关键作用。因此，探索自动分割方法意义重大，其中基于深度学习的方法发展迅速，并在多器官分割领域取得了显著进展。然而，获取规模适当且精细标注的多器官数据集极为困难且昂贵。这种标注稀缺限制了高性能多器官分割模型的发展，却促进了众多标注高效的学习范式。其中，利用外部数据集的迁移学习、使用未标注数据的半监督学习以及整合部分标注数据的部分监督学习等研究，成为突破多器官分割困境的主流途径。本文首先回顾传统全监督方法，然后从技术和方法学两个维度，系统全面地阐述上述三种学习范式在多器官分割中的应用背景，最后总结其面临的挑战与未来趋势。