Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes with Built-in Dice Similarity Coefficient Parameter Optimization Function

PURPOSE: Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce inter-physician variability which is one of the largest sources of uncertainty in head and neck radiotherapy. Besides using uniform margin expansions to auto-delineate high-risk CTVs, very little work h...

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Detalhes bibliográficos
Publicado no:Int J Radiat Oncol Biol Phys
Main Authors: Cardenas, Carlos E., McCarroll, Rachel E., Court, Laurence E., Elgohari, Baher, Elhalawani, Hesham, Fuller, Clifton D., Jomaa, Mona, Meheissen, Mohamed A.M., Mohamed, Abdallah S.R., Rao, Arvind, Williams, Bowman, Wong, Andrew, Yang, Jinzhong, Aristophanous, Michalis
Formato: Artigo
Idioma:Inglês
Publicado em: 2018
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Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7473446/
https://ncbi.nlm.nih.gov/pubmed/29559291
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.ijrobp.2018.01.114
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