Mmi-Unet: Colorectal cancer CT image segmentation based on multi-modal information interaction

Colorectal cancer (CRC) segmentation from computed tomography (CT) images remains challenging, primarily due to low contrast and the irregular morphology of tumorous lesions. Existing multi-modal methods are often constrained by simplistic feature concatenation strategies, which limit the exploitation of collaborative information across modalities. Such limitations become increasingly pronounced when dealing with complex anatomical structures and highly heterogeneous lesions. To address these challenges, we propose a novel multi-modal segmentation model, referred to as multimodal interaction Unet (Mmi-Unet). Our approach employs separate ResNet encoders to extract modality-specific features, thereby preserving their independence, and leverages cross-attention mechanisms along with information entropy to capture inter-modality synergy. In addition, we introduce a dynamic fusion coefficient training module, enabling flexible adjustment of modality fusion ratios to achieve enhanced information integration. Built on a U-Net framework, Mmi-Unet further incorporates multi-scale feature fusion and collaborative optimization. Experimental results on plain and enhanced CRC imaging tasks indicate that our model surpasses existing approaches, achieving Dice coefficients and intersection-over-union (IoU) scores of up to 0.9557, 0.9559, 0.9326, and 0.9435, respectively. These findings demonstrate the superior accuracy and robustness of the proposed model for CRC segmentation.