# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 1 Introduction  
Brain tumors are a type of central nervous system disease that has attracted considerable attention. They differ greatly in biological characteristics and prognosis; therefore, their effective treatment strategies are also different [1]. Brain tumor disease is mainly treated by surgical resection, followed by radiation and chemotherapy [2, 3]. Consequently, preoperative comprehension of position, morphology, dimensions, the extent of peritumal edema, and the interaction of the tumor with adjacent nerve fiber tracts is paramount. Imaging studies play a crucial role as adjuncts in the diagnostic and therapeutic journey of neurosurgeons [4]. The spectrum of brain tumor imaging encompasses modalities such as X-ray, CT, and MRI [5]. The foundation of MRI [6] lies in the disparities of tissue relaxation times and the intrinsic proton density of human tissues, both of which dictate the relative signal intensity variations across different tissues in magnetic resonance imagery, thereby highlighting the attributes of both healthy and pathological tissues. Its superior soft tissue resolution, elevated signal-to-noise ratio, and capability for multiplanar imaging render it the method of choice for brain tumor imaging. In the realm of tumor radiotherapy, it is crucial to precisely outline the tumor margins and the boundaries of adjacent healthy tissuesto establish the treatment area accurately [7]. Precision is essential to protect patients from the effects of excessive radiation. However, achieving accurate contouring of brain tumors is a complex and time-consuming task, often characterized by low levels of reproducibility, precision, and consistency. The advent of artificial intelligence has introduced novel prospects in the realm of medical imaging, augmenting diagnostic assistance for physicians [8]. Its role in aiding the diagnosis of MRI-detected brain lesions is particularly noteworthy, with a significant impact on the development of junior physicians. This development paves the way for more precise and efficient intelligent diagnostic tools in clinical practice moving forward. However, the intricate anatomical structure and critical functionality of the brain, coupled with the uneven grayscale nature of MRI imaging, pose substantial challenges. The variability in tumor location, size, shape, and appearance, along with the alterations in the surrounding brain tissue architecture due to the mass or infiltrative effects of tumors, compound the difficulty of automated detection and segmentation of brain tumors [9]. Attention mechanisms, integrated into the 3D U-Net [10] framework, have substantially boosted the precision of 3D U-Net models. However, devising attention mechanisms that accentuate the functionality of each component within 3D U-Net presents a formidable challenge. Researchers are now delving into the intricacies of seamlessly embedding attention mechanisms into the encoder and decoder of 3D U-Net, aiming to gain a deeper comprehension and enhance the performance of individual elements. This involves fine-tuning the attention weights across various layers, ensuring the network prioritizes critical image features while disregarding less relevant details. By employing this approach, 3D U-Net can enhance its precision in MRI segmentation and deliver more robust and dependable outcomes in intricate contexts, such as medical imaging. Moreover, these tailored attention mechanisms can alleviate the computational load of the model and expedite processing, which is crucial for real-time applications. By addressing the distinct characteristics of each part within the 3D U-Net architecture, the main contributions of this study are as follows:  
1. We propose a PCSAB block for the encoding layer of the 3D U-Net network, such a layer being able to extract more detailed information and integrate global features from the encoding layer, providing more accurate features for the decoding layer. 2.