## 1 Introduction  
A brain tumor is an area of injury or disease within the brain, which can range from small to large, from few to many, from relatively harmless to life threatening. Common intracranial tumors include gliomas, metastases, and meningiomas. Glioma is the most common primary central nervous system tumor [1]. According to the heterogeneity and biological behavior of tumor cells, as well as the presence or absence of vascular proliferation in the tumor body, gliomas are classified into four grades by the WHO. Grades 1-2 represent low-grade malignancy, while grades 3-4 indicate highly malignant tumors [2]. Traditional imaging diagnosis mainly grades gliomas based on their morphology, signal characteristics, degree of enhancement of tumor parenchyma, presence or absence of cystic necrosis, and degree of peritumoral edema [3]. Low-grade gliomas have uniform signals, clear boundaries, and mostly lack enhancement. The characteristics of high-grade brain tumors are typically associated with vasogenic edema, mass effect, and intravenous contrast enhancement [4]. Although there has been significant progress in surgery, radiation therapy, and chemotherapy in recent years, the prognosis still needs to be improved. The purpose of treatment is to remove or eliminate as many tumors as possible while minimizing damage to normal brain tissue, prolonging the patient's life and their quality of life as much as possible. Therefore, segmenting the subregion of the tumor and determining the degree of invasion of the tumor on surrounding structures before surgery can be helpful for selecting treatment plans and formulating the best surgical plan [5]. Magnetic Resonance Imaging (MRI) [6] is currently the preferred method for brain tumor imaging due to its excellent soft tissue resolution, high signal-to-noise ratio, and multi-directional imaging. It usually includes four modalities, T1-Weighted (T1), T1-enhanced contrast (T1 ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) [7], in the [PERSON] 2021 dataset. The four modalities are usually input simultaneously into the program to assist in effective analysis, that is, multimodal brain tumor segmentation. The simple procedure of manual and semi-automatic segmentation can be seen in Figure 1. Typically, radiologists begin by importing brain tumor imaging data. They then proceed to examine the data across the axial, sagittal, and coronal planes to scrutinize the image details. Ultimately, they utilize the region of interest (ROI) for calibration and measurement purposes. Compared to traditional manual and semi-automatic diagnostic methods used by radiologists, brain tumor segmentation methods based on artificial intelligence are characterized by fast segmentation speed, high diagnostic accuracy, and a reduced missed diagnosis rate. UNet [8] is widely used for medical image processing, which fully captures the feature information of images with a special symmetrical structure and uses efficient restoration techniques to segment the lesion site. In order to fully utilize the depth information, a 3D UNet that performs well in the field of medical image segmentation has been proposed by [PERSON] [9] and has been extensively researched. The feature extraction in UNet is mainly undertaken by combining two convolutions, but convolution is characterized by a receptive field that is too small. This seems to be a common challenge; the insufficient extraction of features with a large receptive field can lead to suboptimal segmentation results. The attention mechanism can effectively supplement the insufficient extraction of two convolutional features in UNet. Recently, one of the most popular ideas in Unet or 3D Unet research has been that the attention mechanism is applied after the two convolutions to extract more image features of encoder blocks. Although some attention mechanisms improve the performance of brain tumor segmentation, further improvement is needed.