LKC-NET: A Liver and Tumor Segmentation Method based on Large Kernel Parallel Dilated Convolution

Accurate drug dosage in cancer treatments depends on precise liver and tumor size estimation, but tumors pose challenges for automatic segmentation due to their small, scattered volumes, complex structures, and low contrast against surrounding organs. Conventional U-Net models struggle to capture these microstructural details. To address this, we propose LKC-Net, a liver and tumor segmentation method leveraging large kernel parallel dilated convolutions. The encoder utilizes depth-wise separable large kernels combined with parallel dilated convolutions and squeeze-and-excitation (SE) modules. This architecture enhances the receptive field and improves the capture of small-scale patterns. SE modules filter redundant information, focusing the model on key regions. Experiments on ATLAS, LiTS, and private datasets demonstrate LKC-Net’s superior performance in liver and tumor segmentation tasks.