Segmentation method of X-ray whole spine coronal image based on VGG-Net
Automatic segmentation of the spine and vertebrae from X-ray spine images is a crucial and challenging task in computer-aided spine image analysis and disease diagnosis applications.To further improve the accuracy of spine image segmentation,this paper proposes an improved model based on VGG-Net.Firstly,the VGG16 network is modified by removing the fully connected layers and used as the feature extraction network for U-Net.Secondly,to enhance the detail information of the images,a wavelet decomposition module is introduced into the feature extraction network.Finally,a self-subtracted spatial self-attention module(SUB-SSAM)mechanism is designed in the upsampling network to enhance the network's ability to identify key features.Experimental results show that the improved model achieves a 2.39%improvement in mean intersection over union(mIoU),a 0.96%improvement in recall,and a 1.31%improvement in accuracy compared to the original VGG-Net model.The trained network model can accurately locate each vertebra and segment the vertebral area.
NETL
NSTL
万方数据
