Brain tumor is a serious central nervous system disease, with an increasing incidence rate that poses a significant challenge to global public health security. Due to the complexity of tumor growth site and the sensitivity and heterogeneity of surrounding tissues, treatment of brain tumors is often limited. U-Net and its variants have demonstrated excellent performance in brain tumor image segmentation and are therefore commonly used to assist in the diagnosis of brain tumors. First, this study summarizes U-Net improvement strategies for brain tumor image segmentation tasks, focusing on methods such as attention mechanisms, enhanced residuals, skip connections, and Transformer fusion, and analyzes their improvement effects on Dice coefficient, sensitivity, and specificity. Additionally, the study discusses the frontier research directions of U-Net, such as multimodal fusion, generative adversarial networks (GAN) applications, and self-supervised learning, to address the limitations of current technologies and expand the application scenarios of brain tumor image segmentation. Finally, the study explores the current challenges and issues in the field of brain tumor image segmentation and provides insights into future research directions.