MRI Scan Synthesis Methods based on Clustering and Pix2Pix
Giulia Baldini Melanie Schmidt Charlotte Zäske Liliana L. Caldeira
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 (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 affect 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.