Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study

• Published in: Contrast media and molecular imaging Vol. 2018; no. 2018; pp. 1 - 12
• Main Authors: Li, Qiaoliang, Huang, Bingsheng, Wang, Tian-Fu, Zheng, Liyun, Wong, Ching-Yee Oliver, Feng, Shi-Ting, Ye, Yufeng, Wu, Po-Man, Chen, Zhewei, Huang, Bin, Liu, Yong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; Hindawi Limited
• Subjects: Accuracy; Artificial neural networks; Automation; Brain cancer; Cancer; Cancer therapies; Computed tomography; Contouring; Correlation analysis; Deep Learning; Female; Head & neck cancer; Head and Neck Neoplasms - diagnostic imaging; Humans; Image processing; Image segmentation; International conferences; Male; Medical imaging; Methods; Middle Aged; Models, Theoretical; Neural networks; NMR; Nuclear magnetic resonance; Positron emission; Positron emission tomography; Positron Emission Tomography Computed Tomography; Semantics; Tomography; Tumors

Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients,...