# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (L.Z)  
Yia Yan  
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China; 20222304521008 tu.scu.edu.cn (Y.G. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (X.X. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (L.Z)  
Yia Yan  
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China; 20222304521008 tu.scu.edu.cn (Y.G. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (X.X. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (L.Z)  
Yia Yan  
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China; 20222304521008 tu.scu.edu.cn (Y.G. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (X.X. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (L.Z)  
Yia Yan  
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China; 20222304521008 tu.scu.edu.cn (Y.G. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (X.X. ); [EMAIL_ADDRESS] (Y.Y.Y. ); [EMAIL_ADDRESS] (L.Z)  
Yia Yan  
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, Chinaprevious studies have shown that U-shaped architectures perform well by using encoder-decoder structures to extract image features and classify pixels in 3D medical images [7, 8]. Throughout the years, more U-shaped model structures have been proposed [9, 10]. However, these studies primarily focus on improving tumor segmentation accuracy, and overlook model complexity. As a result, the equipment requirements become higher, resulting in a heavy burden on memory resources, and these model structures often lack effective plug-and-play functionality, limiting their portability. So it is crucial to design a model with low memory burden and high segmentation accuracy. In order to enhance the feature extraction capability and improve the segmentation precision, various attention methods have been proposed in recent years and widely applied to 3D medical image segmentation tasks [11, 12, 13]. Although these attention methods enhance segmentation accuracy, they still require substantial memory and exhibit high computational complexity. This is due to the fact that, during the tasks of 3D medical image segmentation, each voxel is processed individually. This results in high computational costs, potential loss of spatial details, and increased sensitivity to noise and artifacts. We re-evaluate the challenge of improving inference efficiency while maintaining accuracy and propose a novel lightweight attention mechanism referred to as entropy-curvature-based attention (ECA). This mechanism comprises the following three components: a tri-axial strip pooling method (TSP) and two attention modules designed in a cascade, entropy-based attention (EGA), and curvature filter attention (CFA). TSP compresses 3D medical images to reduce computational load and memory requirements. EGA aims to eliminate noise interference from the features after TSP, while CFA aims to efficiently extract local features from the image. The main contributions of this paper are as follows:  
1. A plug-and-play attention model named ECA is presented. It demonstrates strong portability and enhances segmentation accuracy while consuming less memory and parameters. 2. A pooling method and two spatial attention methods are proposed in ECA. The pooling method is utilized to compress 3D images. Meanwhile, the two attention methods are utilized to enhance tumor features and improve segmentation accuracy. 3. A meningioma dataset was constructed, which is supported by West China Hospital. All segmentation labels were manually marked by experienced neurosurgeons and used for the automatic segmentation training of meningioma tumors. The rest of this article is organized as follows. Section 2 discusses related works relevant to this research. Section 3 describes the details of the specific structure of the method.