Liver cancer is one of the most common malignant diseases worldwide. Segmentation and reconstruction of liver tumors and vessels in CT images can provide convenience for physicians in preoperative planning and surgical intervention. In this paper, we introduced a TransFusionNet framework, which consists of a semantic feature extraction module, a local spatial feature extraction module, an edge feature extraction module, and a multi-scale feature fusion module to achieve fine-grained segmentation of liver tumors and vessels. In addition, we applied the transfer learning approach to pre-train using public datasets and then fine-tune the model to further improve the fitting effect. Furthermore, we proposed an intelligent quantization scheme to compress the model weights and achieved high performance inference on JetsonTX2. The TransFusionNet framework achieved mean IoU of 0.854 in vessel segmentation task, and achieved mean IoU of 0.927 in liver tumor segmentation task. When profiling the Computational Performance of the quantized inference, our quantized model achieved 4TFLOPs on Node with NVIDIA RTX3090 and 132GFLOPs on JetsonTX2. This unprecedented segmentation effect solves the accuracy and performance bottleneck of automated segmentation to a certain extent.