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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Detalles Bibliográficos
Autores principales: Xue, Xudong, Qin, Nannan, Hao, Xiaoyu, Shi, Jun, Wu, Ailin, An, Hong, Zhang, Hongyan, Wu, Aidong, Yang, Yidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390915/
https://www.ncbi.nlm.nih.gov/pubmed/32793483
http://dx.doi.org/10.3389/fonc.2020.01134

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390915/
https://www.ncbi.nlm.nih.gov/pubmed/32793483
http://dx.doi.org/10.3389/fonc.2020.01134

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