3D cGAN based cross-modality MR image synthesis for brain tumor segmentation

Abstract

Different modalities of magnetic resonance imaging (MRI) can indicate tumor-induced tissue changes from different perspectives, thus benefit brain tumor segmentation when they are considered together. Meanwhile, it is always interesting to examine the diagnosis potential from single modality, considering the cost of acquiring multi-modality images. Clinically, T1-weighted MRI is the most commonly used MR imaging modality, although it may not be the best option for contouring brain tumor. In this paper, we investigate whether synthesizing FLAIR images from T1 could help improve brain tumor segmentation from the single modality of T1. This is achieved by designing a 3D conditional Generative Adversarial Network (cGAN) for FLAIR image synthesis and a local adaptive fusion method to better depict the details of the synthesized FLAIR images. The proposed method can effectively handle the segmentation task of brain tumors that vary in appearance, size and location across samples.