## 2 Related Work  
In recent years, many researchers have segmented the hippocampus from brain MRI images using machine learning algorithms such as k-means clustering, the watershed algorithm, and the subtractive clustering algorithm [7; 8; 9]. These machine learning algorithms can segment the hippocampus with more accuracy than manual segmentation. However, the segmentation accuracy of machine learning algorithms is limited by image noise and complex brain structure, which makes the segmentation performance very unstable. Recently, deep learning algorithms typified by convolutional neural networks (CNNs), which can automatically capture features, have demonstrated better advantages than machine learning methods in the image processing field. Many studies have shown that CNNs outperform typical semantic segmentation methods [10]. Thus, a range of deep learning technologies are applied to the medical segmentation area, such as in retinal blood vessel segmentation [11], brain tumor segmentation [12; 13; 14; 15], breast cancer segmentation [16], etc. These deep learning technologies can achieve excellent accuracy in hippocampus segmentation tasks through large-scale dataset training. U-Net is a broadly employed deep learning medical image segmentation algorithm. It can integrate both global and local contextual features via the encoder and decoder, then compensate for feature loss resulting from downsampling via skip connections [17]. Owing to U-Net's simple structure and perfect performance, researchers have proposed various variant networks based on U-Net for different application scenarios in recent years. [PERSON] [18] proposed UNet++ based on the UNet structure. UNet++ has more distinct scale skip connections and improved feature concatenation methods than U-Net, enabling it to capture targets of various scales and shapes. R2U-Net [19] is also an extension of U-Net that introduces cyclic and residual connections to improve the network's expression capabilities. Other similar models based on U-Net include Res-UNet [20], MultiResUNet [21], etc. These variant algorithms based on U-Net typically introduce attention mechanisms [22], residual connections [20], or other new network structures to improve the algorithm's segmentation accuracy and robustness on different tasks and datasets. These algorithms provide important tools for the development of medical image segmentation and make its accuracy more accurate, faster, and more reliable. U-Net is suitable for processing 2D but not 3D data. However, many 3D medical images collected by electronic computed tomography (ECT), MRI, ultrasound, and other medical imaging equipment contain more important spatial information and can provide more comprehensive lesion information compared to 2D medical images. These 3D images must be sliced into 2D images before being processed using U-Net, which may result in the loss of key anatomical structure information. To address this issue, a 3D U-Net model is designed, which is similar to U-Net in architecture [23]. The 3D U-Net, which substitutes the 2D convolutional operations of U-Net with 3D volume convolutional operations, is commonly used in the 3D medical image segmentation area. For instance, [PERSON] et al. [24] showed that segmenting the brain tumor using 3D U-Net can enable accurate identification and segmentation of the brain tumor region, which contributes to the advancement of brain tumor diagnosis. V-Net [25], UNETR [26], Swin UNETR [27], and other algorithms have also been designed for 3D image segmentation. These 3D segmentation networks can effectively utilize the three-dimensional information of medical images to achieve more accurate segmentation results, helping doctors to comprehensively understand the spatial distribution and morphological features of lesions to arrange the best treatment plans. To segment targets with excellent precision in medical image segmentation tasks, a network must focus on specific target information while ignoring other unimportant information. The attention mechanism can solve this problem.