Style transfer between Microscopy and Magnetic Resonance Imaging via
  Generative Adversarial Network in small sample size settings
Monika Pytlarz Adrian Onicas Alessandro Crimi
Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic
imaging based on the same tissue samples is promising because it can allow
histopathological analysis in the absence of an underlying invasive biopsy
procedure. Here, we tested a method for generating microscopic histological
images from MRI scans of the corpus callosum using conditional generative
adversarial network (cGAN) architecture. To our knowledge, this is the first
multimodal translation of the brain MRI to histological volumetric
representation of the same sample. The technique was assessed by training
paired image translation models taking sets of images from MRI scans and
microscopy. The use of cGAN for this purpose is challenging because microscopy
images are large in size and typically have low sample availability. The
current work demonstrates that the framework reliably synthesizes histology
images from MRI scans of corpus callosum, emphasizing the network's ability to
train on high resolution histologies paired with relatively lower-resolution
MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can
be used for educational purposes.