  Existing brain tumor segmentation methods usually utilize multiple Magnetic
Resonance Imaging (MRI) modalities in brain tumor images for segmentation,
which can achieve better segmentation performance. However, in clinical
applications, some modalities are missing due to resource constraints, leading
to severe degradation in the performance of methods applying complete modality
segmentation. In this paper, we propose a Multimodal feature distillation with
Convolutional Neural Network (CNN)-Transformer hybrid network (MCTSeg) for
accurate brain tumor segmentation with missing modalities. We first design a
Multimodal Feature Distillation (MFD) module to distill feature-level
multimodal knowledge into different unimodality to extract complete modality
information. We further develop a Unimodal Feature Enhancement (UFE) module to
model the relationship between global and local information semantically.
Finally, we build a Cross-Modal Fusion (CMF) module to explicitly align the
global correlations among different modalities even when some modalities are
missing. Complementary features within and across different modalities are
refined via the CNN-Transformer hybrid architectures in both the UFE and CMF
modules, where local and global dependencies are both captured. Our ablation
study demonstrates the importance of the proposed modules with CNN-Transformer
networks and the convolutional blocks in Transformer for improving the
performance of brain tumor segmentation with missing modalities. Extensive
experiments on the BraTS2018 and BraTS2020 datasets show that the proposed
MCTSeg framework outperforms the state-of-the-art methods in missing modalities
cases. Our code is available at: https://github.com/mkang315/MCTSeg.
