前列腺癌放射治疗的U-Net架构

Q4 Engineering
B. Mendes, Inês Domingues, João A. M. Santos
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引用次数: 0

摘要

前列腺癌(PCa)于1853年首次被诊断为前列腺肝硬化。前列腺癌无症状,通常通过直肠指检(DRE)和前列腺特异性抗原(PSA)水平诊断。自从Imbert和Imbert在1904年首次用x射线治疗晚期前列腺恶性肿瘤以来,外部束放射治疗(EBRT)现在是局部和局部晚期疾病的治疗选择,也是转移性小体积疾病的姑息治疗选择。随着EBRT中计算机的引入和更好的成像技术,体积描绘仍然是一项非常耗时的任务。虽然深度学习方法是迫切需要的,但EBRT系统仍然依赖于手动或半自动分割技术。U-Net架构是专门为医学图像分割而设计的,具有良好的效果。这篇文献综述收集了使用U-Net架构的工作,概述了方法、技术和获得的结果,作为PCa自动分割框架的潜在基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
U-Net Architectures for Prostate Cancer Radiation Therapy
Prostate Cancer (PCa) was first diagnosed in 1853 as cirrhosis of the prostate gland. Silently asymptomatic, PCa is usually diagnosed with Digital Rectal Examination (DRE) and Prostate Specific Antigen (PSA) levels. Since the first treatment of an advanced prostatic malignancy with X-rays by Imbert and Imbert in 1904, External Beam Radiation Therapy (EBRT) is now a curative option for localised and locally advanced disease and a palliative option for the metastatic low-volume disease. With the introduction of computers in EBRT and better imaging techniques, volume delineation is still a very time-consuming task. While Deep Learning methods are urged, EBRT systems still rely on manual or semi-automatic segmentation techniques. The U-Net architecture was specially designed for medical image segmentation presenting promising results. This literature review gathers work using U-Net architectures, outlining methods, techniques and obtained outcomes as a potential foundation for an automated segmentation framework for PCa.
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来源期刊
U.Porto Journal of Engineering
U.Porto Journal of Engineering Engineering-Engineering (all)
CiteScore
0.70
自引率
0.00%
发文量
58
审稿时长
20 weeks
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