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Early and accurate diagnosis of parkinsonian syndromes is critical to provideappropriate care to patients and for inclusion in therapeutic trials. The rednucleus is a structure of the midbrain that plays an important role in thesedisorders. 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 thered nucleus. Current multimodal segmentation algorithms are computationallyconsuming, cannot deal with missing modalities and need annotations for allmodalities. In this paper, we propose a new model that integrates priorknowledge from different contrasts for red nucleus segmentation. The methodconsists of three main stages. First, it disentangles the image into high-levelinformation representing the brain structure, and low-frequency informationrepresenting the contrast. The high-frequency information is then fed into anetwork to learn anatomical features, while the list of multimodallow-frequency information is processed by another module. Finally, featurefusion is performed to complete the segmentation task. The proposed method wasused with several iron-sensitive contrasts (iMag, QSM, R2*, SWI). Experimentsdemonstrate that our proposed model substantially outperforms a baseline UNetmodel when the training set size is very small.

Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI