Open AccessSegmentation of prostate and prostate zones using deep learning
Author(s) -
Olmo Zavala-Romero,
Adrian L. Breto,
Isaac Xu,
Yeon S. Chang,
Nicole Gautney,
Alan Dal Pra,
Matthew C. Abramowitz,
Alan Pollack,
Radka Stoyanova
Publication year - 2020
Publication title -
strahlentherapie und onkologie
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.801
H-Index - 68
eISSN - 1439-099X
pISSN - 0179-7158
DOI - 10.1007/s00066-020-01607-x
Subject(s) - siemens , segmentation , artificial intelligence , prostate , medicine , standard deviation , deep learning , pattern recognition (psychology) , artificial neural network , nuclear medicine , computer science , convolutional neural network , sørensen–dice coefficient , image segmentation , mathematics , statistics , cancer , physics , quantum mechanics
Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors.