## 1 Introduction
Performance variability was a significant issue with methods like the Pulse Coupled Neural Network exhibiting high standard deviations in performance metrics indicating inconsistent results across different test samples. Complex models, such as NAS-UNet and H-DenseUNet, posed implementation challenges, due to their computational intensity making them less practical for real-world clinical applications. Our study addresses these gaps by utilizing a larger and more diverse dataset from the VerSe'20 dataset, which enhances the model's ability to generalize across various patient anatomies and imaging conditions, ensuring robust performance in clinical settings. The Res U-Net model we employed demonstrates stable and consistent performance with lower variability in metrics, providing reliable segmentation results crucial for clinical use. By integrating the [PERSON]'s method for feature extraction, we improve segmentation accuracy effectively separating vertebrae from the background. This approach balances the strengths of deep learning and traditional techniques, achieving superior results, while remaining practical and scalable for clinical implementation, ultimately benefiting patient care and improving medical imaging workflows. In this work, we propose a framework that leverages both deep learning and traditional machine learning methods for the segmentation and identification of vertebrae in CT scans. Our primary focus is on classifying and segmenting lumbar and thoracic vertebrae, followed by feature extraction using the [PERSON]'s method. [PERSON]'s method is an effective thresholding technique that separates the foreground (vertebrae) from the background by maximizing the between-class variance [8]. Our dataset comprises 214 images sourced from VerSe'20 for training and validation purposes and an additional 25 images from actual patient datasets for testing. The proposed approach includes a fully automated binary segmentation of the vertebrae, using a Res U-Net architecture. The pictorial representation is given in Figure 1. This method does not require single vertebrae level annotations for training and making it more versatile and also easier to implement in clinical settings [3]. The performance was evaluated for proposed framework using a dataset of 214 CT scans from the VerSe'20 by achieving a model accuracy of 93.62%. The results highlighted the importance of selecting appropriate neural network architectures for specific medical imaging tasks and demonstrated the potential of combining deep learning with classical techniques to improve segmentation outcomes [7]. By integrating advanced CNN architectures with traditional machine learning algorithms, our framework addresses the challenges associated with vertebrae segmentation and identification. The proposed solution enhances segmentation accuracy and offers a scalable and efficient approach for analyzing large-scale medical imaging datasets [9]. Further, Section 2 details the methodology, covering the data collection process, preprocessing steps like noise reduction and intensity normalization, and the architecture of the Res U-Net model, including its use of deep learning and traditional techniques for vertebrae segmentation. Section 3 presents the model's performance results, evaluating key metrics such as the Dice Similarity Coefficient (DSC), sensitivity, and specificity, while comparing the model to existing segmentation methods. This section also includes visual analyses of segmented images and discusses the clinical implications of the findings. Finally, Section 4 concludes the study by summarizing the results, emphasizing the research  
Figure 1: Segmentation of the vertebrae using Res U-Net. contributions, and suggesting future directions, such as model optimization and real-time clinical applications.