# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 2 Related Work
### Attention Mechanisms for Segmentation  
In the MRI images of the patient's brain, the tissue area of the tumor region is small, and the boundaries between various modalities are not clear. In order to better focus on the tumor region in brain MRI images, many researchers have introduced the attention mechanism into semantic segmentation of medical images [22]. Initially, 2D attention was mainly used for image feature extraction. Since the introduction of squeeze-and-excitation modules in SENet [11], various convolution-based attention methods have emerged. [PERSON] et al. [12] proposed CBAM, which optimizes the features by deriving the attention map along the channel and spatial dimensions. [PERSON] et al. [23] proposed DANet, which captures global dependencies to improve segmentation accuracy through a self-attentive mechanism that integrates spatial and channel attention modules. [PERSON] et al. [24] proposed a lightweight and efficient pseudo-3D model called HDC-Net. [PERSON] et al. [25] introduced an iterative edge attentional network, which uses multiple modules to extract multiscale information, remove noise, and integrate edge and region information to address challenges such as target scale diversity and complex context.  
However, when dealing with 3D images, the problem occurs that 2D attention cannot be used directly. In 3D medical image segmentation, the complete 3D image is usually processed as a series of 2D slices, and a 2D attention mechanism is employed to improve the accuracy and robustness of segmentation, so this approach fails to fully capture the spatial contextual 3D information. In volumetric medical segmentation, a 3D convolution-based attention mechanism can better capture local and global 3D features, enabling the model to establish stronger spatial associations between slices, thereby improving segmentation accuracy. To address this limitation, researchers have begun to explore methods to directly apply attention mechanisms in 3D images. [PERSON] et al. [26] proposed the 3D attention module MHCA, which uses convolution to achieve joint attention in both space and channel. [PERSON] et al. [27] introduced a 3D large-kernel attention module, which combines the advantages of self-attention and convolution to capture both local and global information while optimizing the computational costs. [PERSON] et al. [28] proposed MBANet; this network uses the optimized shuffle unit and the 3D shuffle attention module to process features while using the SA module in skip connections to recover resolution. [PERSON] et al. [13] proposed TDPC-Net to facilitate comprehensive learning of 3D information in brain tumor images. [PERSON] et al. [29] proposed a dynamic sparse attention mechanism named BiFormer, while [PERSON] et al. [30] introduced UNETR++, a model that utilizes an efficient pairwise attention module to reduce computational costs.  
In the context of processing 3D medical images, the problems of a large number of parameters and high memory footprint are inevitably encountered. Previous models also exhibit certain limitations, as shown in Table 1. To address this challenge, we optimize the existing attention frameworks and propose a lightweight and easily transferable attention module.