文章：

深度学习在放射治疗中的自动分割技术

Automatic Segmentation with Deep Learning in Radiotherapy

原文发布日期：1 September 2023

DOI: 10.3390/cancers15174389

类型: Article

开放获取: 是

英文摘要：

This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can research practices in medical image segmentation be improved?”, “What is missing from the current corpus?”, and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today’s competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information.

摘要翻译： 

本综述系统概述了当前放射治疗领域应用深度学习的自动分割研究现状。通过分析涵盖多个癌种部位、影像类型（CT/MRI/PET）及分割方法的807篇已发表文献，我们提取了关键统计数据以揭示该领域的共性特征、发展趋势与方法学特点，并识别出需要进一步深入研究的领域。此外，我们通过提出具有明确导向性的问题对文献体系进行深度剖析，旨在提供高质量且具实践指导意义的见解，包括："研究者启动分割研究时应考虑哪些因素？""如何改进医学图像分割的研究实践？""现有文献体系存在哪些空白？"等问题。基于此，我们为当前竞争激烈的学术环境提供了开展优质分割研究的实践指南，这些指导原则适用于放射治疗各子领域的未来研究。为辅助分析过程，本研究采用了大型语言模型ChatGPT进行信息提炼。

原文链接：

Automatic Segmentation with Deep Learning in Radiotherapy