Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images wit...

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Detalles Bibliográficos
Autores principales: Park, Sohee, Lee, Sang Min, Do, Kyung-Hyun, Lee, June-Goo, Bae, Woong, Park, Hyunho, Jung, Kyu-Hwan, Seo, Joon Beom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2019
Materias:
Thoracic Imaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757001/
https://www.ncbi.nlm.nih.gov/pubmed/31544368
http://dx.doi.org/10.3348/kjr.2019.0212

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757001/
https://www.ncbi.nlm.nih.gov/pubmed/31544368
http://dx.doi.org/10.3348/kjr.2019.0212

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