  Early and accurate diagnosis of parkinsonian syndromes is critical to provide
appropriate care to patients and for inclusion in therapeutic trials. The red
nucleus is a structure of the midbrain that plays an important role in these
disorders. It can be visualized using iron-sensitive magnetic resonance imaging
(MRI) sequences. Different iron-sensitive contrasts can be produced with MRI.
Combining such multimodal data has the potential to improve segmentation of the
red nucleus. Current multimodal segmentation algorithms are computationally
consuming, cannot deal with missing modalities and need annotations for all
modalities. In this paper, we propose a new model that integrates prior
knowledge from different contrasts for red nucleus segmentation. The method
consists of three main stages. First, it disentangles the image into high-level
information representing the brain structure, and low-frequency information
representing the contrast. The high-frequency information is then fed into a
network to learn anatomical features, while the list of multimodal
low-frequency information is processed by another module. Finally, feature
fusion is performed to complete the segmentation task. The proposed method was
used with several iron-sensitive contrasts (iMag, QSM, R2*, SWI). Experiments
demonstrate that our proposed model substantially outperforms a baseline UNet
model when the training set size is very small.
