A slice classification model-facilitated 3D encoder–decoder network for segmenting organs at risk in head and neck cancer

For deep learning networks used to segment organs at risk (OARs) in head and neck (H&N) cancers, the class-imbalance problem between small volume OARs and whole computed tomography (CT) images results in delineation with serious false-positives on irrelevant slices and unnecessary time-consuming...

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Detalles Bibliográficos
Autores principales: Zhang, Shuming, Wang, Hao, Tian, Suqing, Zhang, Xuyang, Li, Jiaqi, Lei, Runhong, Gao, Mingze, Liu, Chunlei, Yang, Li, Bi, Xinfang, Zhu, Linlin, Zhu, Senhua, Xu, Ting, Yang, Ruijie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Oncology/Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779351/
https://www.ncbi.nlm.nih.gov/pubmed/33029634
http://dx.doi.org/10.1093/jrr/rraa094

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779351/
https://www.ncbi.nlm.nih.gov/pubmed/33029634
http://dx.doi.org/10.1093/jrr/rraa094

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