Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
This addresses the tedious and error-prone preprocessing step in clinical brain tumor segmentation, offering a more efficient pipeline, though it is incremental as it builds on existing deep learning methods.
The study tackled the problem of brain extraction (skull-stripping) in automated brain tumor segmentation from MRIs, finding that the choice of brain extraction method can reduce tumor segmentation performance by up to 15.7%, and that training models on non-skull-stripped images achieves competitive results while saving time.
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.