  Disentangling anatomical and contrast information from medical images has
gained attention recently, demonstrating benefits for various image analysis
tasks. Current methods learn disentangled representations using either paired
multi-modal images with the same underlying anatomy or auxiliary labels (e.g.,
manual delineations) to provide inductive bias for disentanglement. However,
these requirements could significantly increase the time and cost in data
collection and limit the applicability of these methods when such data are not
available. Moreover, these methods generally do not guarantee disentanglement.
In this paper, we present a novel framework that learns theoretically and
practically superior disentanglement from single modality magnetic resonance
images. Moreover, we propose a new information-based metric to quantitatively
evaluate disentanglement. Comparisons over existing disentangling methods
demonstrate that the proposed method achieves superior performance in both
disentanglement and cross-domain image-to-image translation tasks.
