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Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

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Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

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Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks. Applied Sciences. 11(2):1-17. https://doi.org/10.3390/app11020844

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/172659

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Título: Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks
Autor: Pellicer-Valero, Oscar J. González-Pérez, Victor Casanova Ramón-Borja, Juan Luis Martín García, Isabel Barrios Benito, María Pelechano Gómez, Paula Rubio-Briones, José Rupérez Moreno, María José Martín-Guerrero, José D.
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials
Fecha difusión:
Resumen:
[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is ...[+]
Palabras clave: Prostate Segmentation , Magnetic Resonance and Ultrasound Images , Convolutional Neural Networks , Neural resolution enhancement , MR prostate imaging , US prostate imaging
Derechos de uso: Reconocimiento (by)
Fuente:
Applied Sciences. (eissn: 2076-3417 )
DOI: 10.3390/app11020844
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/app11020844
Código del Proyecto:
info:eu-repo/grantAgreement/MECD//FPU17%2F01993/
Agradecimientos:
This work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993
Tipo: Artículo

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