Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China.
Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China.
Biomed Eng Online. 2024 Jun 8;23(1):52. doi: 10.1186/s12938-024-01238-8.
Accurate segmentation of multiple organs in 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 methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.
准确地从医学图像中分割出头、颈、胸和腹部的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。在过去的几年中，基于数据驱动的特征提取方法和端到端训练，自动深度学习的多器官分割方法已经远远超过了传统方法，成为了一个新的研究课题。本综述系统地总结了该领域的最新研究。我们使用关键词“多器官分割”和“深度学习”在 Google Scholar 上搜索了 2016 年 1 月 1 日至 2023 年 12 月 31 日期间发表的论文，共检索到 327 篇论文。我们遵循 PRISMA 指南进行论文选择，其中 195 项研究被认为属于本综述的范围。我们总结了多器官分割所涉及的两个主要方面：数据集和方法。关于数据集，我们提供了现有公共数据集的概述，并进行了深入分析。关于方法，我们根据它们是否需要完整的标签信息，将现有的方法分为三大类：完全监督、弱监督和半监督。我们根据分割精度总结了这些方法的成果。在讨论和结论部分，我们概述并总结了多器官分割的当前趋势。
