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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| Główni autorzy: | , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
MDPI AG
2023-03-01
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| Seria: | Bioengineering |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.mdpi.com/2306-5354/10/4/412 |
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