Brain MRI Tumor Segmentation with Adversarial Networks

TL;DR

This paper introduces SegAN-CAT, an adversarial network-based method for brain tumor segmentation in MRI, improving upon previous models by using different inputs, a modified loss function, and transfer learning across modalities, with promising results.

Contribution

The work extends SegAN by incorporating a new input model, a modified loss function, and transfer learning, enhancing segmentation performance especially with limited modality data.

Findings

SegAN-CAT outperforms SegAN with all four MRI modalities available.

Transfer learning improves single-modality MRI segmentation.

The approach shows promising results on large BraTS datasets.

Abstract

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.