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MRI-guided Automated Delineation of Gross Tumor Volume for Nasopharyngeal Carcinoma using Deep Learning

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机构： [1]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen, China [2]Department of Radiation Therapy,The Second Affiliated Hospital,Guangzhou University of Chinese Medicine,Guangzhou, China [3]College of Information Technology,United Arad Emirates University,Al Ain, United Arab Emirates

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关键词： nasopharyngeal carcinoma segmentation multimodality deep learning

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In this paper, we propose a novel deep learningbased automatic delineation method of nasopharynx gross tumor volume (GTVnx) by combing computed tomography (CT) andmagnetic resonance imaging (MRI) modalities. The purpose of this study is to explore whether MRI can provide additional information to improve the accuracy of delineation on CT. The proposed model can adaptively leverage the high contrast information of MRI into the automated delineation of GTVnx on CT in nasopharyngeal carcinoma (NPC) radiotherapy. In this study, the dataset collected from 192 patients with NPC was used to verify the performance of the proposed method. The average Dice Similarity Coefficient, 95% Hausdorff Distance and Average Symmetric Surface Distance of the segmentation results predicted by the proposed model are 0.7181, 9.6637mm, and 2.8014mm, respectively, which outperformed that of the single-modal and the concatenation-based multi-modal segmentation models.

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第一作者机构： [1]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen, China

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