■宫颈癌临床目标体积(CTV)概述和处于危险中的器官分割是宫颈癌诊断和治疗的关键步骤。手动分割效率低且主观,导致自动化或半自动化方法的发展。然而,图像质量的限制,器官运动,个体差异仍然构成重大挑战。除了对医学图像分割的大量研究之外,该领域缺乏全面审查。本文的目的是全面回顾不同类型的宫颈癌医学图像分割的文献,讨论当前分割过程中的水平和挑战。
■截至2023年5月31日,我们对GoogleScholar进行了全面的文献检索,PubMed,和WebofScience使用以下术语组合:“宫颈癌图像”,\"分段\",和“大纲”。纳入的研究集中在利用计算机断层扫描(CT)分割宫颈癌,磁共振(MR),和正电子发射断层扫描(PET)图像,由两名独立的研究者筛选资格。
■本文回顾了有关CTV和宫颈癌风险器官分割的代表性论文,并根据图像模态将方法分为三类。全面描述了传统或深度学习方法。分析了相关方法的异同,并对它们的优点和局限性进行了深入的讨论。我们还通过使用我们的私有数据集来验证所选方法的性能,从而纳入了实验结果。结果表明,残差模块和挤压激励块模块可以显着提高模型的性能。此外,基于改进水平集的分割方法比其他方法具有更好的分割精度。
■该论文提供了对当前宫颈癌CTV概述和处于危险中的器官分割的最新技术的宝贵见解,突出未来研究的领域。
UNASSIGNED: Cervical cancer clinical target volume (CTV) outlining and organs at risk segmentation are crucial steps in the diagnosis and treatment of cervical cancer. Manual segmentation is inefficient and subjective, leading to the development of automated or semi-automated methods. However, limitation of image quality, organ motion, and individual differences still pose significant challenges. Apart from numbers of studies on the medical images\' segmentation, a comprehensive
review within the field is lacking. The purpose of this paper is to comprehensively
review the literatures on different types of medical image segmentation regarding cervical cancer and discuss the current level and challenges in segmentation process.
UNASSIGNED: As of May 31, 2023, we conducted a comprehensive literature search on Google Scholar, PubMed, and Web of Science using the following term combinations: \"cervical cancer images\", \"segmentation\", and \"outline\". The included studies focused on the segmentation of cervical cancer utilizing computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) images, with screening for eligibility by two independent investigators.
UNASSIGNED: This paper reviews representative papers on CTV and organs at risk segmentation in cervical cancer and classifies the methods into three categories based on image modalities. The traditional or deep learning methods are comprehensively described. The similarities and differences of related methods are analyzed, and their advantages and limitations are discussed in-depth. We have also included experimental results by using our private datasets to verify the performance of selected methods. The results indicate that the residual module and squeeze-and-excitation blocks module can significantly improve the performance of the model. Additionally, the segmentation method based on improved level set demonstrates better segmentation accuracy than other methods.
UNASSIGNED: The paper provides valuable insights into the current state-of-the-art in cervical cancer CTV outlining and organs at risk segmentation, highlighting areas for future research.