Prostate cancer is the second-most frequently diagnosed cancer and the sixth
leading cause of cancer death in males worldwide. The main problem that
specialists face during the diagnosis of prostate cancer is the localization of
Regions of Interest (ROI) containing a tumor tissue. Currently, the
segmentation of this ROI in most cases is carried out manually by expert
doctors, but the procedure is plagued with low detection rates (of about
27-44%) or overdiagnosis in some patients. Therefore, several research works
have tackled the challenge of automatically segmenting and extracting features
of the ROI from magnetic resonance images, as this process can greatly
facilitate many diagnostic and therapeutic applications. However, the lack of
clear prostate boundaries, the heterogeneity inherent to the prostate tissue,
and the variety of prostate shapes makes this process very difficult to
automate.In this work, six deep learning models were trained and analyzed with
a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and
Universitat Politecnica de Catalunya. We carried out a comparison of multiple
deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention
Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy
loss function. The analysis was performed using three metrics commonly used for
image segmentation: Dice score, Jaccard index, and mean squared error. The
model that give us the best result segmenting all the zones was R2U-Net, which
achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error,
respectively