MMMT-Net: Multi-Modal Multi-Temporal Network for Liver Tumor Segmentation from CT Images

Accurate liver tumor segmentation is critical for the diagnosis and treatment of liver cancer, yet remains challenging due to image quality and tumor heterogeneity. Existing methods predominantly focus on single-phase and single-time-point CT scans, neglecting complementary multi-phase information and longitudinal correlations. In this paper, we propose the first multi-modal multi-temporal network, which integrates diverse information from arterial phase and longitudinal data to facilitate liver tumor segmentation of venous phase. Our approach mainly comprises three novel blocks: (1) A polynomial residual block that captures abundant tumor characteristics via nonlinear hierarchical feature learning; (2) A longitudinal phase attention block that refines input features from channel and spatial dimensions, and recalibrates the target feature based on inter-phase and inter-temporal dependencies; (3) A progressive up-sampling block that preserves structural details during resolution restoration, and forms an effective decoder architecture. Experiments on a large-scale CT dataset of longitudinal multi-phase liver tumors demonstrate that the proposed network achieves promising liver tumor segmentation and outperforms state-of-the-art methods. In addition, an ablation study validates the functions of the proposed blocks.