# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 3 Methodology
### Parallel Channel and Spatial Attention Block (PCSAB)  
In the process of brain tumor MRI image segmentation, the encoding process is an important step for obtaining features. Effective feature extraction is crucial for the final segmentation; in particular, the effective fusion of global and local features plays a  
Figure 1: An illustration of the proposed MEASegNet for brain tumor image segmentation. significant role. However, the effective extraction of global and local features is one of the current research challenges. The PSCAB module can enhance the fusion and extraction of details and global information by using spatial and channel attention as optimization means. A diagram of the PSCAB block is shown in Figure 2. It mainly consists of three parts: the channel attention mechanism (the upper part in Figure 2), the original information retention part (the middle part), and the spatial attention mechanism (the lower part). Channel attention first uses global average pooling to obtain as much global information as possible, then maps the image with global feature information to one-dimensional features, called \(F_{map}\) operation, and uses a one-dimensional convolution with a kernel size of 3 and a stride of 1 to capture the interaction information between different features, learning the complex relationships between local features. Then, the information is restored to three-dimensional information, and the channel weights are obtained through the Sigmoid activation function. The process of obtaining global features and transforming three-dimensional data into one-dimensional data can be mathematically expressed as follows:  
\[X_{map}=F_{map}(Global\,AvgPool(x)) \tag{1}\]  
where \(x\) represents the input data, while \(X_{map}\) denotes the output result of the mapping operation. \(Global\,AvgPool\) denotes global average pooling and \(F_{map}\) indicates the mapping operation. The operation of integrating detailed information and restoring one-dimensional data to three-dimensional data can be defined as follows:  
\[x^{{}^{\prime}}=F_{anti-map}\big{(}Conv_{1d}\big{(}X_{map}\big{)}\big{)} \tag{2}\]  
where \(x^{{}^{\prime}}\) denotes the output result of this operation, while \(F_{anti-map}\) indicates the transformation that restores one-dimensional data back to its three-dimensional form. \(Conv_{1d}\) denotes a one-dimensional convolution with a kernel size of 3 and a stride of 1. Channel attention can be represented as follows:  
\[X_{channel}=Sigmoid\big{(}x^{{}^{\prime}}\big{)} \tag{3}\]  
Figure 2: An illustration of the building blocks of the PSCAB block. where \(x^{{}^{\prime}}\) represents the input data, while \(X_{channel}\) represents the output result of channel attention. In the lower half of the PSCAB module lies the spatial attention mechanism. Initially, average pooling and maximum pooling are employed to retain comprehensive information and accentuate local salient features, with a particular focus on edge details. Subsequently, the feature information derived from both average and maximum pooling is concatenated. This combined feature set is then processed through a convolutional layer featuring a 3 \(\times\) 3 \(\times\) 3 kernel and a stride of 1, enabling the extraction of refined features. Ultimately, the resultant feature map, enhanced by spatial attention, is generated via the Sigmoid activation function. Spatial attention can be described as follows:  
\[X_{spatial}=Sigmoid(Conv_{3\times 3\times 3}(concate(maxpool(x),avgpool(x)))) \tag{4}\]  
where \(x\) represents the input data. The \(maxpool\) and \(avgpool\) terms can be respectively expressed as maximum pooling and average pooling. \(Conv_{3\times 3\times 3}\) represents a convolution operation with a kernel size of 3 \(\times\) 3 \(\times\) 3 and a stride of 1. After the convolution, InstanceNorm normalization and LeakyReLU activation function operations are applied. To capture features that elude spatial and channel attention mechanisms, the PSCAB module preserves the original information in its central section.