基于 VGG-Net 的 X 射线全脊柱冠状面图像分割方法

在计算机辅助脊柱图像分析和疾病诊断应用中，从X 射线脊柱图像中自动分割脊柱和椎骨是一个关键且具有挑战性 的问题。为进一步提升脊柱图像分割精度，提出一种基于 VGG-Net 改进的模型。首先，将 VGG16 网络去掉了后面的全连接 层，用作 U-Net 的特征提取网络；其次，为了增强图像的细节信息，在特征提取网络引入小波分解模块；最后，在上采样网络中 设计了一种逐像素相减的自空间注意力模块(SUB-SSAM) 机制，进一步提高网络模型识别关键特征的能力。实验结果表明， 改进后的模型相较于原VGG-Net 模型在平均交并比(mloU) 上提高了2.39%、召回率(recall)提高了0.96%、准确率(accura- cy)提高了1.31%,训练的该网络模型可以定位到每一块椎骨，准确分割椎体区域。

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(mloU),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.