Research On Brain Tumor Segmentation Based On Multiple Attention Mechanisms

Glioma is the most common primary brain tumor with high morbidity and mortality,which seriously endangers human life and health.With the development of medical imaging technology,medical imaging has become an important tool to assist doctors in medical diagnosis and researches.As one of these techniques,Magnetic Resonance Imaging(MRI)is widely used for brain imaging for its advantages of non-invasiveness,good spatial resolution and soft tissue resolution.Brain tumor segmentation helps physicians to make early diagnosis,treatment planning and prognosis assessment of patients,but manual segmentation is time-consuming and laborious,and can be affected by the skill level of physicians.Therefore,it is important to develop accurate automatic brain tumor segmentation techniques.However,brain tumors are highly heterogeneous and exhibit heterogeneity of gray values and irregularity of shapes in multimodal MRI brain images,and there is a serious class imbalance problem in brain tumor segmentation,so it is a challenging task to develop accurate and reliable automatic segmentation algorithms.In recent years,many deep learning-based methods have been applied to brain tumor segmentation with good results.For the application of deep learning in brain tumor segmentation,this paper discusses the related techniques in depth and proposes a brain tumor segmentation model based on multiple attention mechanisms.The specific research components and innovative work are as follows:1、A novel brain tumor segmentation network based on Multiple Attention U-Net(MA-Net)is proposed,which fully utilizes attention to spatial information,channel information and scale information.The use of attention not only extracts richer semantic information,but also focuses more on the information of small brain tumors.thus improving the segmentation of brain tumors.2、In the final layer of the 3D U-Net encoder,a Dual attention module(DAM)is added,in which a Spatial attention block(SAB)and a Channel attention block(CAB)are concatenated to highlight the salient feature information while eliminating irrelevant and noisy feature responses.In the upsampling process,a Multi-attention gate module(MAGM)is proposed to fuse the feature maps from the encoder,the output of the dual-attention module,and the feature maps of the current decoder to make full use of the multi-scale image features for accurate segmentation of brain tumors.3、Data enhancements such as scaling,contrast enhancement and Gaussian noise are used in the training process to alleviate the overfitting phenomenon caused by the small dataset.And a fusion loss function based on Dice loss and Cross Entropy loss is used.The Cross Entropy loss can help the network to accelerate the convergence and keep the training stable.Dice loss can alleviate the class imbalance phenomenon.4、Extensive experiments are conducted in the BraTS2018 dataset.The effectiveness of each module is verified by ablation experiments and compared with other state-of-the-art methods,the experimental results show that MAU-Net achieved better segmentation results.