Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network

PURPOSE: To introduce an end-to-end automatic segmentation model for organs at risk (OARs) in thoracic CT images based on modified DenseNet, and reduce the workload of radiation oncologists. MATERIALS AND METHODS: The computed tomography (CT) images of 36 lung cancer patients were included in this s...

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Detalles Bibliográficos
Autores principales: Zhang, Fuli, Wang, Qiusheng, Yang, Anning, Lu, Na, Jiang, Huayong, Chen, Diandian, Yu, Yanjun, Wang, Yadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8964972/
https://www.ncbi.nlm.nih.gov/pubmed/35371991
http://dx.doi.org/10.3389/fonc.2022.861857

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8964972/
https://www.ncbi.nlm.nih.gov/pubmed/35371991
http://dx.doi.org/10.3389/fonc.2022.861857

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