## 1 Introduction  
The hippocampus is a portion of the brain located between the cerebral thalamus and the medial temporal lobe; it is a crucial organ that is responsible for storing and organizing memory [1]. Research on the main function and basic structure of the hippocampus is essential for understanding the working principles of the brain and the pathogenesis of neurodegenerative diseases and developing treatment methods. Many studies have shown that the shape and texture of the hippocampus are related to neurodegenerative diseases such as alzheimer's disease (AD), epilepsy, etc. To some extent, atrophy of the hippocampus can reflect the condition of these diseases [2; 3]. Magnetic resonance imaging (MRI) is a new medical imaging examination technique that creates high-definition images of organs and tissues by using powerful magnetic fields and harmless radio waves [4]. MRI has become increasingly crucial in disease diagnosis and research due to the rapid development of neuroimaging technologies [5]. This technology can not only help clinicians detect lesions but also provide more accurate information on the location and size of lesions, providing significant assistance in disease diagnosis. Accurately segmenting the hippocampus from brain MRI images and measuring its volume and morphological characteristics can provide an essential foundation for early diagnosis, progress monitoring, and treatment evaluation of these diseases. Thus, clinicians usually observe the shape of the hippocampus to diagnose neurodegenerative diseases and conduct surgical planning and treatment evaluation. As a result, precisely segmenting the hippocampus from brain MRIs and observing its shape are critical for disease diagnosis. Segmenting the hippocampus from MRI images is a challenging task, as the quality of the images may vary, the shape of the hippocampus is irregular, and the hippocampus boundary is not distinct. Furthermore, manually segmenting the hippocampus from brain MRIs is a professional task that needs to be carried out by experienced experts or clinicians. Thus, accurately and automatically segmenting the hippocampus from brain MRI images rather than manually segmenting has recently drawn a lot of attention. MRI images are 3D data that contain more information than 2D images. Typically, networks comprise numerous parameters and a high level of computational complexity when processing 3D data such as MRI images, which may consume considerable computational and storage resources. A number of lightweight networks have been proposed to reduce the network's scale but may also limit their performance [6]. Thus, a 3D U-Net-based segmentation model named Coordinate Attention and Dynamic Convolution U-Net (CADyUNet) is introduced, which significantly improves the network's performance without expanding its scale by combining coordinate attention mechanisms and dynamic convolution operations. We applied the CADyUNet to hippocampus segmentation tasks and confirmed its effectiveness. The major contributions of this study are listed as follows:  
* To maintain a balance between the model's performance and scale, a dynamic convolution block named dy-block is designed, which introduces new dynamic convolution operations to substitute the normal convolution operations and spatial dropout blocks to reduce the risk of overfitting. The dy-block can segment the hippocampus, especially its boundary, more precisely and quickly without increasing the depth of the network, which is defined as the number of hidden layers, or the width of the network, which is defined as the number of channels in each hidden layer;
* To improve the segmentation performance, an improved coordinate attention mechanism is utilized in 3D U-Net.