  State-of-the-art brain tumor segmentation is based on deep learning models
applied to multi-modal MRIs. Currently, these models are trained on images
after a preprocessing stage that involves registration, interpolation, brain
extraction (BE, also known as skull-stripping) and manual correction by an
expert. However, for clinical practice, this last step is tedious and
time-consuming and, therefore, not always feasible, resulting in
skull-stripping faults that can negatively impact the tumor segmentation
quality. Still, the extent of this impact has never been measured for any of
the many different BE methods available. In this work, we propose an automatic
brain tumor segmentation pipeline and evaluate its performance with multiple BE
methods. Our experiments show that the choice of a BE method can compromise up
to 15.7% of the tumor segmentation performance. Moreover, we propose training
and testing tumor segmentation models on non-skull-stripped images, effectively
discarding the BE step from the pipeline. Our results show that this approach
leads to a competitive performance at a fraction of the time. We conclude that,
in contrast to the current paradigm, training tumor segmentation models on
non-skull-stripped images can be the best option when high performance in
clinical practice is desired.
