scholarly journals Rectal Content and Intrafractional Prostate Gland Motion Assessed by Magnetic Resonance Imaging

2011 ◽  
Vol 52 (2) ◽  
pp. 199-207 ◽  
Author(s):  
Ichiro OGINO ◽  
Tetsuji KANEKO ◽  
Ryoko SUZUKI ◽  
Tonika MATSUI ◽  
Shigeo TAKEBAYASHI ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Resonance Imaging ◽  
Rectal Content

Diffusion-weighted and perfusion-weighted magnetic resonance imaging of the prostate gland of healthy adult dogs

2019 ◽  
Vol 80 (9) ◽  
pp. 832-839 ◽  
Author(s):  
Florian Willmitzer ◽  
Francesca Del Chicca ◽  
Patrick R. Kircher ◽  
Adriano Wang-Leandro ◽  
Peter W. Kronen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Healthy Adult ◽  
Prostate Gland ◽  
Resonance Imaging ◽  
Diffusion Weighted ◽  
Weighted Magnetic Resonance Imaging

Magnetic Resonance Imaging of the Internal Anatomy of the Prostate Gland

1987 ◽  
Vol 22 (12) ◽  
pp. 947-953 ◽  
Author(s):  
D BRADLEY KOSLIN ◽  
PHILIP J. KENNEY ◽  
ROBERT E. KOEHLER ◽  
JERROLD A. VAN DYKE
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Internal Anatomy ◽  
Resonance Imaging

scholarly journals Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

2021 ◽  
Vol 11 (2) ◽  
pp. 782 ◽  
Author(s):  
Albert Comelli ◽  
Navdeep Dahiya ◽  
Alessandro Stefano ◽  
Federica Vernuccio ◽  
Marzia Portoghese ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Central Processing ◽  
Prostate Segmentation

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

Magnetic resonance imaging of the prostate gland

1990 ◽  
Vol 12 (1) ◽  
pp. 109-114 ◽  
Author(s):  
Mitchell D. Schnall ◽  
Howard M. Pollack
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Resonance Imaging

Prostate gland motion assessed with cine-magnetic resonance imaging (cine-MRI)

2005 ◽  
Vol 62 (2) ◽  
pp. 406-417 ◽  
Author(s):  
Michel J. Ghilezan ◽  
David A. Jaffray ◽  
Jeffrey H. Siewerdsen ◽  
Marcel Van Herk ◽  
Anil Shetty ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Cine Magnetic Resonance Imaging ◽  
Cine Mri ◽  
Resonance Imaging
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