Simple Summary Prostate cancer represents a highly prevalent form of cancer worldwide,
with timely detection and treatment being crucial for achieving a high survival rate. Manual …

Background The development of deep learning (DL) models for prostate segmentation on
magnetic resonance imaging (MRI) depends on expert-annotated data and reliable …

Background Prostate segmentation is an essential step in computer-aided detection and
diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good …

World-wide incidence rate of prostate cancer has progressively increased with time
especially with the increased proportion of elderly population. Early detection of prostate …

Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate
cancer. However, manual three-dimensional (3D) segmentation of the prostate is a …

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

Featured Application The study demonstrates that high-speed deep learning networks could
perform accurate prostate delineation facilitating the adoption of novel imaging parameters …

Objective To develop an accurate and automatic segmentation model based on convolution
neural network to segment the prostate and its lesion regions. Methods Of all 180 subjects …

Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a
challenging task due to the high variability of prostate anatomic structure. Artifacts such as …

In the present work, we present a publicly available, expert-segmented representative
dataset of 158 3.0 Tesla biparametric MRIs [1]. There is an increasing number of studies …