Liver Tumor Segmentation and Radio Frequency Ablation Treatment Design Based on CT Image

In order to accurately realize the ablation treatment of liver tumors, it is necessary to design a surgical treatment plan for radio frequency ablation (RFA) based on computed tomography (CT) image. Currently, as a popular deep learning method, U-net network is widely used for image segmentation. However, traditional U-net network cannot achieve to segment the liver tumor from the CT images. In order to accomplish the tumor segmentation in CT images and make the design on the RFA treatment, in this paper we propose an improved U-net network. We use two deep convolutional neural networks based on U-net and improve the overall performance of the segmentation network by improving the network model. On the basis of liver tumor segmentation, we finally accomplish the design of RFA for liver tumor removal. Simulation results show that our proposed method can effectively achieve liver tumor segmentation and obtain a good Dice coefficient, thus providing help for the surgical design of RFA for liver tumors.