Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation

BACKGROUND: Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations ha...

Descripción completa

Detalles Bibliográficos
Autores principales: Wahid, Kareem A., Sahin, Onur, Kundu, Suprateek, Lin, Diana, Alanis, Anthony, Tehami, Salik, Kamel, Serageldin, Duke, Simon, Sherer, Michael V., Rasmussen, Mathis, Korreman, Stine, Fuentes, David, Cislo, Michael, Nelms, Benjamin E., Christodouleas, John P., Murphy, James D., Mohamed, Abdallah S. R., He, Renjie, Naser, Mohammed A., Gillespie, Erin F., Fuller, Clifton D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491357/
https://www.ncbi.nlm.nih.gov/pubmed/37693394
http://dx.doi.org/10.1101/2023.08.30.23294786

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491357/
https://www.ncbi.nlm.nih.gov/pubmed/37693394
http://dx.doi.org/10.1101/2023.08.30.23294786

Ejemplares similares