  Segmentation of regions of interest in images of patients, is a crucial step
in many medical procedures. Deep neural networks have proven to be particularly
adept at this task. However, a key question is what type of deep neural network
to choose, and whether making a certain choice makes a difference. In this
work, we will answer this question for the task of segmentation of the Organs
At Risk (OARs) in radiation treatment of cervical cancer (i.e., bladder, bowel,
rectum, sigmoid) in Magnetic Resonance Imaging (MRI) scans. We compare several
state-of-the-art models belonging to different architecture categories, as well
as a few new models that combine aspects of several state-of-the-art models, to
see if the results one gets are markedly different. We visualize model
predictions, create all possible ensembles of models by averaging their output
probabilities, and calculate the Dice Coefficient between predictions of
models, in order to understand the differences between them and the potential
of possible combinations. The results show that small improvements in metrics
can be achieved by advancing and merging architectures, but the predictions of
the models are quite similar (most models achieve on average more than 0.8 Dice
Coefficient when compared to the outputs of other models). However, the results
from the ensemble experiments indicate that the best results are obtained when
the best performing models from every category of the architectures are
combined.
