STD-Net: a spatio-temporal decoupling network for multiphasic liver lesion segmentation and characterization

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality, where accurate imaging-based diagnosis plays a central role in guiding treatment. Multiphasic CT and MRI provide dynamic information about lesion enhancement, yet most existing deep learning methods treat different phases as simple channels and fail to capture their temporal evolution. In this work, we introduce STD-Net, a spatio-temporal decoupling network that explicitly separates spatial feature extraction from temporal dynamics modeling. A shared-weight 3D encoder learns robust anatomical representations, while a transformer-based temporal module captures sequential contrast patterns such as arterial hyperenhancement and venous washout. This design mirrors clinical reasoning and reduces the entanglement of spatial appearance, temporal change, and motion artifacts. Comprehensive experiments on TCGA-LIHC, LiTS, and MSD datasets show that STD-Net consistently outperforms state-of-the-art baselines in both segmentation and characterization, achieving higher Dice, lower HD95, and superior classification accuracy. Qualitative analyses and distributional evaluations further confirm that our approach offers more stable and generalizable performance, particularly for small or low-contrast lesions. These findings demonstrate the potential of spatio-temporal decoupling as a general paradigm for dynamic medical imaging.