  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.
