Background: Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a consistent and accurate automatic segmentation method is urgently needed. Methods: To meet the above challenges, we propose an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet. In our study, the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module is added to each decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise. Results: We used the BraTS2020 challenge online verification tool to evaluate our approach. The focus of verification is that the Dice scores of the whole tumor (WT), tumor core (TC) and enhanced tumor (ET) are 0.68, 0.85 and 0.70, respectively. Conclusion: Although MRI images have different intensities, AGSE-VNet is not affected by the size of the tumor, and can more accurately extract the features of the three regions, it has achieved impressive results and made outstanding contributions to the clinical diagnosis and treatment of brain tumor patients.

翻译：Glioma是最常见的大脑恶性肿瘤,发病率高,死亡率超过3%,严重危及人体健康。在诊所获取脑肿瘤的主要方法是MRI。从多式MRI扫描图像中分离脑肿瘤区域有助于治疗检查、诊断后监测和病人影响评估。但是,临床脑肿瘤分解的常见操作仍然是人工分解,导致不同操作者之间耗时和产生巨大的性能差异,迫切需要一种一致和准确的自动分解方法。方法:为了应对上述挑战,我们提议了一个自动的脑肿瘤MRI数据分解框架,称为AGSE-VNet。在我们的研究中,从多式MRI扫描图像中分离脑肿瘤区域和Excite(SE)模块被添加到每个编码器中,注意指南过滤器模块被添加到每个分解器中,利用频道关系自动增强频道中的有用信息以抑制无用的信息,并使用关注机制来指导边缘信息并消除不相关信息的影响,如噪音。结果:我们用SEUEM MA 5 和 IMER 分别用S 进行强化的测试,而 AS AS IML IML 3 和 IMU IMU 正在不断 进行升级 的测试。(我们所使用的系统 3 正在升级 的常规 的测试 和 和 的 的测试是整个的升级 。