# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 3 Methodology
### Channel Reduce Residual Atrous Spatial Pyramid Pooling Block (CRRASPP)  
Situated at the terminal end of the encoder, the bottleneck layer emerges after the input image has undergone a cascade of downsampling and convolutional transformations. In this process, the spatial dimensions of the input image undergo a marked contraction, while there is a concomitant surge in the number of channels. The iterative downsampling imparts to the bottleneck layer's feature maps an expansive receptive field, enabling them to encompass the vast majority of the input image, thereby encapsulating global contextual information. This endows the model with an enhanced capacity to comprehend the overarching structure and semantic nuances of the image, thus establishing a robust groundwork for the ensuing decoder to execute precise segmentation tasks. Functioning as a pivotal conduit between the encoder and decoder, the bottleneck layer facilitates the transfer of high-level features, meticulously extracted by the encoder, to the decoder. This transfer is instrumental in providing the decoder with crucial sustenance for its subsequent undertakings of upsampling and the intricate process of feature amalgamation. Nevertheless, considering the inherent complexity and variety of features within the bottleneck layer, adeptly capturing features across multiple scales serves to amplify their expressive capabilities. Consequently, this enrichment renders the features more aptly tailored for the ensuing processes of upsampling and segmentation, thereby elevating the overall efficacy of these tasks.  
As shown in Figure 3, to capture multiscale features, in the CRRASPP module, a convolution with a kernel size of 1 \(\times\) 1 \(\times\) 1, a stride of 1, and dilated convolutions with a kernel size of 3 \(\times\) 3 \(\times\) 3, a stride of 1, and dilation rates of 2, 4, and 6 are first employed to extract multiscale features. In order to reduce the number of model parameters, the number of channels in the image is reduced to a quarter of the original following each dilated convolution. Subsequently, the four dilated convolutions with different dilation rates are concatenated and then aggregated via a convolution with a kernel size of \(1\times 1\times 1\) and a stride of 1. To enhance the feature representation capability, the features are abstracted and subjected to further learning in conjunction with the input data features, thereby preserving the key information. The CRRASPP block can be described as follows:  
\[U_{out}=x+Conv_{1\times 1\times 1}\big{(}concate\big{(}Conv_{1\times 1\times 1 },DConv_{3\times 3\times 3}(x,\theta_{i})\big{)}\big{)},\ \theta_{i}\in\{2,4,6\} \tag{6}\]  
where \(U_{out}\) represents the output features processed by the CRRASPP attention mechanism, \(Conv_{1\times 1\times 1}\) refers to a convolution with a kernel size of \(1\times 1\times 1\) and a stride of 1, the concatenating operation indicates the concatenation operation, and \(DConv_{3\times 3\times 3}\) describes a dilated convolution with a kernel size of \(3\times 3\times 3\), a stride of 1, and a dilation rate of \(\theta_{i}\), where the value of the dilation rate \(\theta\) is \(\{2,4,6\}\).