  We consider a missing data problem in the context of automatic segmentation
methods for Magnetic Resonance Imaging (MRI) brain scans. Usually, automated
MRI scan segmentation is based on multiple scans (e.g., T1-weighted,
T2-weighted, T1CE, FLAIR). However, quite often a scan is blurry, missing or
otherwise unusable. We investigate the question whether a missing scan can be
synthesized. We exemplify that this is in principle possible by synthesizing a
T2-weighted scan from a given T1-weighted scan. Our first aim is to compute a
picture that resembles the missing scan closely, measured by average mean
squared error (MSE). We develop/use several methods for this, including a
random baseline approach, a clustering-based method and pixel-to-pixel
translation method by Isola et al. (Pix2Pix) which is based on conditional
GANs. The lowest MSE is achieved by our clustering-based method. Our second aim
is to compare the methods with respect to the effect that using the synthesized
scan has on the segmentation process. For this, we use a DeepMedic model
trained with the four input scan modalities named above. We replace the
T2-weighted scan by the synthesized picture and evaluate the segmentations with
respect to the tumor identification, using Dice scores as numerical evaluation.
The evaluation shows that the segmentation works well with synthesized scans
(in particular, with Pix2Pix methods) in many cases.
