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Manual segmentation is often tedious and time consuming. Thus, an automatic segmentation algorithm is expected to solve this problem. However, because different areas are scanned, the number of spines in the original CT image and the coverage area are often different, making it extremely difficult to directly conduct a fully autonomous spine segmentation. In this study, we propose a two\u2010stage automatic spine segmentation method based on 3D Swin Transformer. In the first stage, the 3D Swin\u2010YoloX algorithm is used to achieve an accurate positioning of each spine segment in the CT images. In the second stage, 3D Swin\u2010UNet is used to achieve a high\u2010precision segmentation of the spine. Using an open dataset, the average Dice of our approach can reach 0.942 and the average Hausdorff distance can reach 6.24, indicating a higher accuracy in comparison with other published methods. 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