Brain MRI Tumor Segmentation with Adversarial Networks [article]

Edoardo Giacomello, Daniele Loiacono, Luca Mainardi
2020 arXiv   pre-print
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
more » ... atasets, 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.
arXiv:1910.02717v2 fatcat:bf4zwamknbeijgp56rr6f4fj5e