| 用于肿瘤调强放射治疗影像分析与转换的深度学习方法 |
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| 引用本文： | 刘国才,顾冬冬,刘骁,刘劲光,刘焰飞,张毛蛋.用于肿瘤调强放射治疗影像分析与转换的深度学习方法[J].中国生物医学工程学报,2022,41(2):224-237. |
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| 作者姓名： | 刘国才 顾冬冬 刘骁 刘劲光 刘焰飞 张毛蛋 |
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| 作者单位： | 1(湖南大学电气与信息工程学院,长沙 410082)2(机器人视觉感知与控制技术国家工程研究中心,长沙 410082) |
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| 基金项目： | 国家自然科学基金(62071176,61671204,61271382)；;湖南省科技计划重点研发专项(2016WK2001)； |
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| 摘 要： | 癌症已成为严重威胁人类健康的主要公共卫生问题。60%~70%的癌症患者需要进行放射治疗。调强放疗是当前主要的临床放疗技术。对近几年基于深度学习的影像分析与转换方法在肿瘤调强放疗计划中的应用进展及关键技术进行综述,包括计算机断层扫描(CT)、锥形束CT(CBCT)、磁共振成像(MRI)、正电子发射断层扫描(PET)引导的肿瘤调强放疗技术应用现状与发展趋势,肿瘤CT/CBCT/MRI/PET影像放疗靶区分割、影像配准以及转换深度卷积神经网络、生成对抗网络的有监督或无监督学习方法,并对未来的研究方向进行展望。
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| 关 键 词： | 医学图像分割 医学图像配准 医学图像转换 深度学习 肿瘤调强放疗 |
| 收稿时间： | 2020-11-16 |
Deep Learning Methods for Image Analysis and Synthesis for Intensity Modulated Radiotherapy:a Review |
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| Liu Guocai,Gu Dongdong,Liu Xiao,Liu Jinguang,Liu Yanfei,Zhang Maodan.Deep Learning Methods for Image Analysis and Synthesis for Intensity Modulated Radiotherapy:a Review[J].Chinese Journal of Biomedical Engineering,2022,41(2):224-237. |
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| Authors: | Liu Guocai Gu Dongdong Liu Xiao Liu Jinguang Liu Yanfei Zhang Maodan |
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| Institution: | (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)(National EngineeringResearch Center for Robot Visual Perception and Control Technology, Changsha 410082, China) |
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| Abstract: | Cancer is a common problem that seriously threatens human health. 60% to 70% of cancer patients need radiotherapy. Currently intensity modulated radiotherapy (IMRT) is one main radiotherapy technique that is widely applied in clinics. This paper reviewed deep learning methods, key technologies and future directions for IMRT, including clinical CT/CBCT/MRI/PET-guided IMRT technologies, and supervised or unsupervised deep convolutional neural networks or generative adversarial networks for the segmentation, registration and image-to-image translation of CT/CBCT/MRI/PET images of tumors. |
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| Keywords: | medical image segmentation medical image registration image-to-image translation deep learning intensity modulated radiotherapy |
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