A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study,...

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Detalles Bibliográficos
Autores principales: Men, Kuo, Boimel, Pamela, Janopaul‐Naylor, James, Cheng, Chingyun, Zhong, Haoyu, Huang, Mi, Geng, Huaizhi, Fan, Yong, Plastaras, John P., Ben‐Josef, Edgar, Xiao, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Radiation Oncology Physics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333147/
https://www.ncbi.nlm.nih.gov/pubmed/30418701
http://dx.doi.org/10.1002/acm2.12494

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333147/
https://www.ncbi.nlm.nih.gov/pubmed/30418701
http://dx.doi.org/10.1002/acm2.12494

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