Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network

OBJECTIVE: This paper intends to propose a method of using TransResSEUnet2.5D network for accurate automatic segmentation of the Gross Target Volume (GTV) in Radiotherapy for lung cancer. METHODS: A total of 11,370 computed tomograms (CT), deriving from 137 cases, of lung cancer patients under radio...

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Detalles Bibliográficos
Autores principales: Xie, Hui, Chen, Zijie, Deng, Jincheng, Zhang, Jianfang, Duan, Hanping, Li, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652981/
https://www.ncbi.nlm.nih.gov/pubmed/36371220
http://dx.doi.org/10.1186/s12967-022-03732-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652981/
https://www.ncbi.nlm.nih.gov/pubmed/36371220
http://dx.doi.org/10.1186/s12967-022-03732-w

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