Deep convolutional neural networks for automatic segmentation of thoracic organs‐at‐risk in radiation oncology – use of non‐domain transfer learning

PURPOSE: Segmentation of organs‐at‐risk (OARs) is an essential component of the radiation oncology workflow. Commonly segmented thoracic OARs include the heart, esophagus, spinal cord, and lungs. This study evaluated a convolutional neural network (CNN) for automatic segmentation of these OARs. METH...

Descripción completa

Detalles Bibliográficos
Autores principales: Vu, Charles C., Siddiqui, Zaid A., Zamdborg, Leonid, Thompson, Andrew B., Quinn, Thomas J., Castillo, Edward, Guerrero, Thomas M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Radiation Oncology Physics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324695/
https://www.ncbi.nlm.nih.gov/pubmed/32602187
http://dx.doi.org/10.1002/acm2.12871

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324695/
https://www.ncbi.nlm.nih.gov/pubmed/32602187
http://dx.doi.org/10.1002/acm2.12871

Ejemplares similares