Sequential and Iterative Auto-Segmentation of High-Risk Clinical Target Volume for Radiotherapy of Nasopharyngeal Carcinoma in Planning CT Images

Sequential and Iterative Auto-Segmentation of High-Risk Clinical Target Volume for Radiotherapy of Nasopharyngeal Carcinoma in Planning CT Images

Background: Accurate segmentation of tumor targets is critical for maximizing tumor control and minimizing normal tissue toxicity. We proposed a sequential and iterative U-Net (SI-Net) deep learning method to auto-segment the high-risk primary tumor clinical target volume (CTVp1) for treatment plann...

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Bibliographic Details
Main Authors: Xudong Xue, Nannan Qin, Xiaoyu Hao, Jun Shi, Ailin Wu, Hong An, Hongyan Zhang, Aidong Wu, Yidong Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-07-01
Series:Frontiers in Oncology
Subjects:
auto-segmentation
radiotherapy
clinical target volume
deep learning
nasopharyngeal carcinoma
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.01134/full

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https://www.frontiersin.org/article/10.3389/fonc.2020.01134/full

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