  Prostate segmentation from magnetic resonance imaging (MRI) is a challenging
task. In recent years, several network architectures have been proposed to
automate this process and alleviate the burden of manual annotation. Although
the performance of these models has achieved promising results, there is still
room for improvement before these models can be used safely and effectively in
clinical practice. One of the major challenges in prostate MR image
segmentation is the presence of class imbalance in the image labels where the
background pixels dominate over the prostate. In the present work we propose a
DL-based pipeline for cropping the region around the prostate from MRI images
to produce a more balanced distribution of the foreground pixels (prostate) and
the background pixels and improve segmentation accuracy. The effect of
DL-cropping for improving the segmentation performance compared to standard
center-cropping is assessed using five popular DL networks for prostate
segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net.
The proposed smart-cropping outperformed the standard center cropping in terms
of segmentation accuracy for all the evaluated prostate segmentation networks.
In terms of Dice score, the highest improvement was achieved for the U-net+ and
ResU-net++ architectures corresponding to 8.9% and 8%, respectively.
