  Deep Learning is a promising approach to either automate or simplify several
tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an
approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based
on Adversarial Networks. In particular, we extend SegAN, successfully applied
to the same task in a previous work, in two respects: (i) we used a different
model input and (ii) we employed a modified loss function to train the model.
We tested our approach on two large datasets, made available by the Brain Tumor
Image Segmentation Benchmark (BraTS). First, we trained and tested some
segmentation models assuming the availability of all the major MRI contrast
modalities, i.e., T1-weighted, T1 weighted contrast-enhanced, T2-weighted, and
T2-FLAIR. However, as these four modalities are not always all available for
each patient, we also trained and tested four segmentation models that take as
input MRIs acquired only with a single contrast modality. Finally, we proposed
to apply transfer learning across different contrast modalities to improve the
performance of these single-modality models. Our results are promising and show
that not SegAN-CAT is able to outperform SegAN when all the four modalities are
available, but also that transfer learning can actually lead to better
performances when only a single modality is available.
