Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

OBJECTIVES: To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI. METHODS: In 2017, 284...

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Detalles Bibliográficos
Autores principales: Schelb, Patrick, Wang, Xianfeng, Radtke, Jan Philipp, Wiesenfarth, Manuel, Kickingereder, Philipp, Stenzinger, Albrecht, Hohenfellner, Markus, Schlemmer, Heinz-Peter, Maier-Hein, Klaus H., Bonekamp, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Urogenital
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755653/
https://www.ncbi.nlm.nih.gov/pubmed/32767102
http://dx.doi.org/10.1007/s00330-020-07086-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755653/
https://www.ncbi.nlm.nih.gov/pubmed/32767102
http://dx.doi.org/10.1007/s00330-020-07086-z

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