U-Trans Net: a hybrid of CNN and transformer network for liver and liver tumor segmentation

Due to problems such as edge blurriness and noise interference in liver medical image segmentation, there are still some challenges for automated liver and liver tumor segmentation. To address these problems, we propose a hybrid network called U-Trans Net, which combines Convolutional Neural Networks (CNNs) and Transformer models. In the encoding stage, it utilizes parallelly CNN and Transformer branches for multi-scale feature extraction, allowing the fusion of global local and local information in a hierarchical manner. The resulting enhanced feature representation is then used for prediction through decoding. Experimental results on the LiTS-ISBI2017 dataset demonstrated that our method could improve segmentation accuracy.