Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer

PURPOSE: The aim of this study was to propose and evaluate a novel three-dimensional (3D) V-Net and two-dimensional (2D) U-Net mixed (VUMix-Net) architecture for a fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC)–delineated contours. METHODS: We collected the computed...

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Detalles Bibliográficos
Autores principales: Jin, Linzhi, Chen, Qi, Shi, Aiwei, Wang, Xiaomin, Ren, Runchuan, Zheng, Anping, Song, Ping, Zhang, Yaowen, Wang, Nan, Wang, Chenyu, Wang, Nengchao, Cheng, Xinyu, Wang, Shaobin, Ge, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9339638/
https://www.ncbi.nlm.nih.gov/pubmed/35924169
http://dx.doi.org/10.3389/fonc.2022.892171

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9339638/
https://www.ncbi.nlm.nih.gov/pubmed/35924169
http://dx.doi.org/10.3389/fonc.2022.892171

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