# Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model
###### Abstract
(2021) directly used a Transformer in a sequence of image blocks to perform image classification tasks, achieving state-of-the-art performance on multiple image recognition benchmarks and successfully applying the Transformer to the field of computer vision. [PERSON] et al. (2021) proposed RDCTrans U-Net, which is based on ResNeXt50, expands the receptive field with complementing dilated convolutions, and introduces the Transformer part in the encoder to improve feature extraction efficiency. However, the internal structure of the model is complex and difficult to understand, and the Transformer part cannot represent positional information in the semantic space well. For a 2D network, it is difficult to utilize the 3D spatial information of liver slices and automatically segment liver 3D images such as CT and MRI ([PERSON] et al., 2020). To address this issue, [PERSON], [PERSON] et al. (2020) proposed a 3D U-Net in 2016, which replaces all 2D operations in the U-Net architecture with corresponding 3D operations to generate 3D segmentation images, effectively utilizing the spatial information between adjacent liver slices and achieving better segmentation results. [PERSON] et al. proposed a H-Dense U-Net in 2018, which combines the features of the 2D Dense U-Net and the 3D Dense U-Net, accelerating the convergence of the 3D Dense U-Net. Meanwhile, dense connection blocks were embedded in the U-Net, further improving the segmentation accuracy for the liver and tumor. [PERSON] et al. (2020) added a 3D attention module to the 3D segmentation model, improving the efficiency of the 3D model. [PERSON] et al. (2020) generalized the 3D model for many image segmentation tasks. However, the 3D model requires a large number of parameters, which puts relatively high demands on computer memory and performance. With the same computational resources, 2D models can have deeper network structures and larger filter receptive fields. Although the abovementioned methods have achieved effective detection, they have not effectively utilized the contextual information of the lesion area during network training. They only extract the global features of the image, failing to learn the deep features of different lesion areas. Moreover, the local features of the lesion area are not emphasized, and they are heavily influenced by irrelevant information, neglecting the auxiliary diagnostic information in the image. Due to the inconsistent shape of liver tumors, small lesion area proportions, and difficulty in distinguishing lesions in the edge region, it is challenging to extract features and achieve accurate segmentation. Focusing on the lesion area itself and utilizing the contextual information from the surrounding region to enhance and complement the lesion features involves comprehensive and thorough extraction of both the global and local features of the lesion area while minimizing the influence of background noise. This approach aims to prevent the loss of details and features in the boundary region of the lesion, making it a promising research direction. In this study, we propose a liver tumor segmentation model that improves the performance of the U-Net. The model accurately localizes lesions and enhances the segmentation performance.