7T to 3T domain adaptation in white matter lesion segmentation on T2-weighted (T2-w) FLAIR images using deep learning

In this work, we explored the domain adaptation problem in deep learning segmentation. Specifically, we applied the residual U-net 1 on 3T and 7T Fluid Attenuated Inverse Recovery (FLAIR) images to delineate the white matter hyperintensity (WMH) in a 2D fashion. We leveraged learning without forgetting 2 to regulate the network’s learning in the new domain to preserve the model’s performance on the old domain while still achieving satisfying results on the new domain images.