  With the development of image segmentation in computer vision, biomedical
image segmentation have achieved remarkable progress on brain tumor
segmentation and Organ At Risk (OAR) segmentation. However, most of the
research only uses single modality such as Computed Tomography (CT) scans while
in real world scenario doctors often use multiple modalities to get more
accurate result. To better leverage different modalities, we have collected a
large dataset consists of 136 cases with CT and MR images which diagnosed with
nasopharyngeal cancer. In this paper, we propose to use Generative Adversarial
Network to perform CT to MR transformation to synthesize MR images instead of
aligning two modalities. The synthesized MR can be jointly trained with CT to
achieve better performance. In addition, we use instance segmentation model to
extend the OAR segmentation task to segment both organs and tumor region. The
collected dataset will be made public soon.
