# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 2 Related Work
### The Attention-Based Module for Medical Image Segmentation  
The attention mechanism has attracted widespread attention due to its ability to extract important feature information while ignoring information that is not helpful for segmentation. Incorporating attention mechanisms into the U-Net architecture to enhance the performance of medical image segmentation has garnered the attention of many researchers. In 2022, [PERSON] et al. [17] proposed RAAGR2-Net, which is based on U-Net. In the network, to minimize the loss of positional information in different modules, they introduced a Residual Atrous Spatial Pyramid Pooling (RASPP) module. Additionally, they utilized an Attention Gate (AG) module to effectively emphasize and recover segmentation outputs from the extracted feature maps. In 2023, [PERSON] et al. [18] developed an efficient 3D segmentation model based on the U-Net structure, named DPAFNet. The network integrated a Dual Path (DP) module to expand the network scale, a Multiscale Attention Fusion (MAF) module to obtain feature maps rich in semantic information, and a 3 DIterative Dilation Convolution Merge (IDCM) module to enhance contextual awareness. Through ablation experiments, they concluded that their network improved segmentation accuracy. [PERSON] et al. [19] used group convolution and shuffle operations to obtain feature maps of the current channels and formed the BU module. They also created a new attention layer in the encoder using multibranch 3D Stochastic Attention (SA), and the resulting MBANet showed significant improvement in segmentation performance compared to other networks. [PERSON] et al. [20] designed MimicNet, incorporating a fine-grained attention module in each subtask of the network to achieve region-aware multimodal fusion, and then obtained accurate positional information through multiscale context. [PERSON] et al. [21] developed region-attention fusion (RAF) in the U-Net architecture which can effectively fuse images of different modalities. [PERSON] et al. [22] embedded a coordinate attention module before the upsampling operation in the 3D U-Net, thereby enhancing the ability to capture local textural feature information and global positional feature information. In 2024, [PERSON] et al. [23] developed an attention module for optimizing the segmentation boundary region which improved the efficiency of feature extraction. [PERSON] et al. [24] proposed an MD-Unet, a U-shaped medical image segmentation network based on the Mixed Depth Convolution Residual Module (MDRM). In this network, the MDRM constructed with Mixed Depth Convolution Attention Blocks (MDAB) captures local and global dependencies in the image to mitigate the impact of intraclass differences. [PERSON] et al. [25] integrated attention mechanism modules in the bottleneck layer and during the upsampling operation, enhancing the correlation between different layers of the network. [PERSON] et al. [26] introduced multi-scale context blocks capable of extracting rich feature information and attention-guided blocks that reduce the impact of learning redundant features, achieving good results. [PERSON] et al. [27] introduced 3D attention mechanisms into the 3D U-Net framework, promoting the comprehensive learning of three-dimensional information in brain tumor MRI images and obtaining excellent segmentation results.  
Corresponding to 3D U-Net and attention mechanisms is the Transformer architecture, which has attracted widespread attention from many researchers due to its excellent ability to extract global feature information, and a large number of improvements have been made from the perspective of medical image segmentation. Although the Transformer evolved from self-attention, its architecture is more complex compared to that of 3D U-Net and attention mechanism modules, and it requires a relatively larger amount of original image data. Therefore, there are still many researchers conducting in-depth studies on the integration of attention mechanisms within the 3D U-Net architecture.