Comparative analysis of automatic segmentation of esophageal cancer using 3D Res-UNet on conventional and 40-keV virtual mono-energetic CT Images: a retrospective study

OBJECTIVES: To assess the performance of 3D Res-UNet for fully automated segmentation of esophageal cancer (EC) and compare the segmentation accuracy between conventional images (CI) and 40-keV virtual mono-energetic images (VMI(40 kev)). METHODS: Patients underwent spectral CT scanning and diagnose...

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Detalles Bibliográficos
Autores principales: Zhong, Hua, Li, Anqi, Chen, Yingdong, Huang, Qianwen, Chen, Xingbiao, Kang, Jianghe, You, Youkuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Computational Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358343/
https://www.ncbi.nlm.nih.gov/pubmed/37483982
http://dx.doi.org/10.7717/peerj.15707

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358343/
https://www.ncbi.nlm.nih.gov/pubmed/37483982
http://dx.doi.org/10.7717/peerj.15707

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