Cargando…
Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach
PURPOSE: To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflow. METHODS AND MATERIALS: Computed tomography (CT)...
Autores principales: | Cardenas, Carlos E., Beadle, Beth M., Garden, Adam S., Skinner, Heath D., Yang, Jinzhong, Joo Rhee, Dong, McCarroll, Rachel E., Netherton, Tucker J., Gay, Skylar S., Zhang, Lifei, Court, Laurence E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9472456/ https://www.ncbi.nlm.nih.gov/pubmed/33068690 http://dx.doi.org/10.1016/j.ijrobp.2020.10.005 |
Ejemplares similares
-
Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow
por: Hernandez, Soleil, et al.
Publicado: (2021) -
Automatic detection of contouring errors using convolutional neural networks
por: Rhee, Dong Joo, et al.
Publicado: (2019) -
Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy
por: Gay, Skylar S., et al.
Publicado: (2023) -
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
por: Court, Laurence E., et al.
Publicado: (2018) -
Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques
por: Rhee, Dong Joo, et al.
Publicado: (2022)