Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

BACKGROUND AND PURPOSE: Currently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radio...

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Detalles Bibliográficos
Autores principales: Luximon, Dishane C, Neylon, John, Lamb, James M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020677/
https://www.ncbi.nlm.nih.gov/pubmed/36937493
http://dx.doi.org/10.1016/j.phro.2023.100427

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020677/
https://www.ncbi.nlm.nih.gov/pubmed/36937493
http://dx.doi.org/10.1016/j.phro.2023.100427

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