## 1 Introduction  
The human spine is a complex anatomical structure consisting of 33 vertebrae that play a crucial role in supporting the body and protecting the spinal cord. Accurate segmentation and identification of vertebrae in medical imaging, such as computed tomography (CT) scans, are essential for diagnosing and treating various spinal pathologies which include fractures, deformities, and degenerative diseases [1]. Traditional methods for spine segmentation involve model-fitting techniques, which can be time-consuming and require extensive manual intervention [2]. Recent advances in deep learning have revolutionized medical image analysis offering automated solutions that significantly improve the efficiency and accuracy of segmentation tasks [3]. Several studies have demonstrated the effectiveness of convolutional neural networks (CNNs) in medical image segmentation [4]. U-Net has become the standard architecture for biomedical image segmentation, due to their ability to capture both local and global context information [5]. The V-Net is a fully convolutional neural network that has also shown promising results in volumetric medical image segmentation [6]. Moreover, approaches that combine deep learning with classical machine learning techniques, such as k-means clustering and k-nearest neighbors (k-NN)have been proposed to enhance segmentation accuracy and robustness [7]. The U-Net architecture introduced by [PERSON] et al. [8] is a significant innovation widely used in various segmentation tasks. It resembles the autoencoder [9] in that it comprises a contracting path and an expansive path. U-Net incorporates skip connections that link different features between the encoder and decoder which enables it to perform multi-scale image-to-image transformations across various resolutions. This capability allows U-Net to work effectively with limited training samples by addressing the challenge of scarce medical image segmentation data and class imbalance. Numerous enhancements to U-Net have been proposed [10; 11; 12; 13; 14; 15; 16; 17], such as incorporating residual connections to mitigate gradient issues leading to successful applications in retinal vessel segmentation [12], brain tumor segmentation [13], and optic nerve head segmentation [14]. In medical segmentation, it is essential to focus on attention mechanisms, which help emphasize important features by dynamically adjusting input weights to prioritize critical information. Originally proposed by [PERSON] et al. [18] for machine translation, the self-attention mechanism has been increasingly adopted in various fields including text processing, speech recognition, and image processing. [PERSON] et al. [19] introduced self-attention to computer vision demonstrating the effectiveness of non-local operations on videos and images in the spatio-temporal dimension. Attention gates have been applied to medical image segmentation by researchers such as [PERSON] et al. [17], [PERSON] et al. [20] and [PERSON] et al. [21] effectively fusing multi-level features. However, these networks often do not fully utilize contextual information and may overlook useful channel features. NAS-UNet was introduced by [PERSON] et al. [22] which employs neural architecture search to automatically design high-performance models tailored for medical image segmentation. Similarly [23] developed the ERDUnet, an efficient residual double-coding U-Net which enhances segmentation accuracy and computational efficiency. Another significant contribution is the H-DenseUNet [24], which combines dense connectivity and U-Net to improve the segmentation of liver and tumor from CT volumes. Previous studies on vertebrae segmentation often faced limitations like small sample sizes, which hindered the generalizability and robustness of their methods. For example, some approaches were tested on as few as three CT scans, limiting their applicability in diverse clinical scenarios.