Medical spine CT images have low segmentation accuracy due to uneven vertebral bone density, complex vertebral structure and low imaging resolution. To tackle these problems, this study proposes a segmentation method for spine CT images with a convolutional-deconvolutional neural network. The multi-scale residual module and the attention mechanism are introduced to improve the U-Net network, and the feature model is trained and tested. Experimental results on real data sets show that this method can effectively improve the accuracy and the efficiency of spine CT image segmentation. The estimated results of Dice coefficient and Intersection Over Union (IOU) are 0.97 and 0.94, respectively.