SECT-Net: hybrid dual-decoder network with SE-convolution transformer for liver tumor segmentation

To comprehensively assess the effectiveness and generalization capability of SECT-Net, extensive experiments were conducted on the liver tumor datasets collected from Quzhou People's Hospital. On the arterial phase dataset, SECT-Net demonstrated excellent segmentation performance, achieving Dice of 0.8452, Mcc of 0.8411, and Jaccard of 0.7339. Similarly, on the portal venous phase dataset, SECT-Net maintained robust generalization, with Dice of 0.8425, Mcc of 0.8396, and Jaccard of 0.7339. Furthermore, on the public 3DIRCADb dataset, SECT-Net also achieved competitive performance, with Dice, Mcc, and Jaccard scores of 0.8845, 0.8855 and 0.7969. These consistent results across both private and public datasets further demonstrate the strong reliability, robustness, and generalization capability of SECT-Net in segmenting liver tumors with diverse intensity distributions and morphological characteristics.