Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

OBJECTIVES: To develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting. METHODS: A consecutive cohort (n = 292) from a prospective database of low-risk patients eligib...

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Detalles Bibliográficos
Autores principales: Arif, Muhammad, Schoots, Ivo G., Castillo Tovar, Jose, Bangma, Chris H., Krestin, Gabriel P., Roobol, Monique J., Niessen, Wiro, Veenland, Jifke F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Magnetic Resonance
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599141/
https://www.ncbi.nlm.nih.gov/pubmed/32594208
http://dx.doi.org/10.1007/s00330-020-07008-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599141/
https://www.ncbi.nlm.nih.gov/pubmed/32594208
http://dx.doi.org/10.1007/s00330-020-07008-z

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