Learning intra-inter-modality complementary for brain tumor segmentation

Multi-modal MRI has become a valuable tool in medical imaging for diagnosing and investigating brain tumors, as it provides complementary information from multiple modalities. However, traditional methods for multi-modal MRI segmentation using UNet architecture typically fuse the modalities at an early or mid-stage of the network, without considering the inter-modal feature fusion or dependencies. To address this, a novel CMMFNet (cross-modal multi-scale fusion network) is proposed in this work, which explores both intra-modality and inter-modality relationships in brain tumor segmentation. The network is built on a transformer-based multi-encoder and single-decoder structure, which performs nested multi-modal fusion for high-level representations of different modalities. Additionally, the proposed CMMFNet uses a focusing mechanism that extracts larger receptive fields more effectively at the low-level scale and connects them to the decoding layer effectively. The multi-modal feature fusion module nests modality-aware feature aggregation, and the multi-modal features are better fused through long-term dependencies within each modality in the self-attention and cross-attention layers. The experiments showed that our CMMFNet outperformed state-of-the-art methods on the BraTS2020 benchmark dataset in brain tumor segmentation.