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Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients

机译：基于深度学习的自动描绘总肿瘤卷和涉及头部和颈部癌症患者PET / CT图像中的节点

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Yngve Mardal Moe; Aurora Rosvoll Groendahl; Oliver Tomic; Einar Dale; Eirik Malinen; Cecilia Marie Futsaether;
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年度 2021
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1. Interobserver Variability in the Delineation of Gross Tumour Volume and Specified Organs-at-risk During IMRT for Head and Neck Cancers and the Impact of FDG-PET/CT on Such Variability at the Primary Site [J] . Shivakumar Gudi, Sarbani Ghosh-Laskar, Jai Prakash Agarwal, Journal of medical imaging and radiation sciences . 2017,第2期

机译：Interobserver在IMRT中造成巨大肿瘤体积和特定的器官 - 患者的危险性变异性以及FDG-PET / CT对初级部位的这种变异性的影响
2. A quantitative comparison of gross tumour volumes delineated on [18F]-FDG PET-CT scan and CECT scan in head and neck cancers [J] . Manickam G Venkada, Sheh Rawat, PS Choudhury, Indian Journal of Nuclear Medicine . 2012,第2期

机译：[18F] -FDG PET-CT扫描和CECT扫描在头颈癌中描述的总肿瘤体积的定量比较
3. Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study [J] . Bin Huang, Zhewei Chen, Po-Man Wu, Contrast media & molecular imaging . 2018,第4期

机译：使用深度学习在PET-CT上全自动描绘头颈癌总肿瘤体积的双中心研究
4. Two-Stage Approach for Segmenting Gross Tumor Volume in Head and Neck Cancer with CT and PET Imaging [C] . Simeng Zhu, Zhenzhen Dai, Ning Wen Challenge Conference on 3D Head and Neck Tumor Segmentation in PET/CT Challenge;International Conference on Medical Image Computing and Computer Assisted Intervention . 2020

机译：用CT和PET成像分割头颈癌粗肿瘤体积的两阶段方法
5. National and Institutional Outcomes Data in Head and Neck Cancers, and Functional and Patient Reported Outcomes in Patients with Oropharyngeal Cancers. [D] . Kansal, Ankit. 2017

机译：头颈癌的国家和机构结局数据，以及口咽癌患者的功能和患者报告结局。
6. Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study [O] . Bin Huang, Zhewei Chen, Po-Man Wu, 2018

机译：使用深度学习在PET-CT上全自动描绘头颈癌总肿瘤体积的双中心研究
7. Progressive Generative Adversarial Networks: Deep Learning in Head and Neck Cancer CT Images to Synthesized PET Images Generation for Hybrid PET/CT Application [O] . Bin HUANG, Zhe-wei CHEN, Martin LAW, 2018

机译：渐进生成的对抗网络：头部和颈部癌症CT图像的深度学习，用于杂交PET / CT应用的合成PET图像

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients

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