基于Non-local的腰椎间盘突出患者多裂肌分割方法

Non-local-based segmentation of multifidus muscle in patients with lumbar disc herniation

为实现腰椎间盘突出患者多裂肌病灶部位的精确分割,提出了一种基于Non-local的腰椎间盘突出患者多裂肌分割方法。以U-Net网络为基础,通过构造混合池化卷积来代替编码器传统的卷积模块,以提高全局特征与局部特征之间的相关性并融合网络高低维特征,增强了网络提取多裂肌特征的能力。然后,在网络的中间部署了一个由2个级联卷积组成的卷积模块。最后,经过由Non-local模块和3×3的卷积构成的解码器,通过引入注意力机制来更加关注目标的特征并抑制不必要的特征和噪音,从而提高模型的性能。实验结果表明,本方法与经典U-Net算法相比,Dice系数提升了9.5%,Jaccard相似系数提升了11.3%,Hausdorff Distance下降了74.6%。该方法提高了多裂肌脂肪浸润部位的分割精度,为腰椎间盘突出患者多裂肌病灶部位的分割提供了一种有效的方法。

In order to achieve accurate segmentation of multifidus muscle lesion sites in patients with lumbar disc herniation,a Non-local-based segmentation method was proposed for the multifidus muscle.Based on the U-Net network,the ability of the network to extract multi-cleft muscle features was enhanced by constructing a hybrid pooling convolution instead of the traditional convolution module of the encoder to improve the correlation between global and local features and to fuse the high and low dimensional features of the network.Then,a convolutional module consisting of two cascaded convolutions was deployed in the middle of the network.Finally,after a decoder consisting of a Non-local module and the convolution,the performance of the model was improved by introducing an attention mechanism to focus more on the features of the target and suppressing unnecessary features and noise.According to the experimental results,the proposed model demonstrates a significant improvement in performance compared to the classical U-Net algorithm.Specifically,it increases the Dice coefficient by 9.5%,Jaccard similarity coefficient by 11.3%,and reduces Hausdorff Distance by 74.6%.This method improves the segmentation accuracy of the fatty infiltration site of the multifidus muscle,and provides an effective method for segmentation of multifidus lesion sites in patients with lumbar disc herniation.