U-Net Architecture for Prostate Segmentation: The Impact of Loss Function on System Performance

Segmentation of the prostate gland from magnetic resonance images is rapidly becoming a standard of care in prostate cancer radiotherapy treatment planning. Automating this process has the potential to improve accuracy and efficiency. However, the performance and accuracy of deep learning models var...

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Detalles Bibliográficos
Autores principales: Montazerolghaem, Maryam, Sun, Yu, Sasso, Giuseppe, Haworth, Annette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10135670/
https://www.ncbi.nlm.nih.gov/pubmed/37106600
http://dx.doi.org/10.3390/bioengineering10040412

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10135670/
https://www.ncbi.nlm.nih.gov/pubmed/37106600
http://dx.doi.org/10.3390/bioengineering10040412

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