Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.

Journal: Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]

Published Date: Mar 27, 2020

PURPOSE: Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors.

Olmo Zavala-Romero

University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States.
Adrian L Breto

University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States.
Isaac R Xu

Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Yu-Cherng C Chang

University of Miami Miller School of Medicine, Miami, FL, USA.
Nicole Gautney

University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States.
Alan Dal Pra

Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Matthew C Abramowitz

Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Alan Pollack

University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States.
Radka Stoyanova

University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States.

Algorithms Datasets as Topic Deep Learning Humans Imaging, Three-Dimensional Magnetic Resonance Imaging Male Prostate Stochastic Processes

View on PubMed Access via DOI PubMed (32221622)