基于嵌入注意力机制的X光脊椎骨角点定位模型

X-Ray Spine Corner Localization using an Embedded Attention Mechanism-Based Model

脊柱侧弯是当今社会中常见的脊柱疾病,在X光图像上快速而准确地定位脊椎骨角点并计算其Cobb角度数是医生诊断脊柱弯曲程度的金指标。针对X光骨科图片中其他器官的遮挡以及复杂背景干扰等问题,提出一种基于嵌入注意力机制和向量损失模块的神经网络模型。所提模型以vertebra-focused landmark detection(VFLD)网络为基础网络,在编码器和解码器之间嵌入旋转注意力机制模块加强网络对于脊椎骨深层、高维特征的提取,抑制其他器官的干扰,同时利用向量相似性的损失函数对网络进行训练。实验结果表明,在MICCAI 2019公开脊椎挑战赛数据集中,所提模型的对称平均绝对百分比误差准确度高达9.31,可以有效提高原模型检测椎骨角点能力。与现有的诸多模型相比,其具有较高的准确率和稳健性。

Scoliosis is a common spinal disease in the current society.Therefore,it is important for doctors to diagnose the degree of spinal curvature to quickly and accurately locate the spinal bone corners on Xray images and calculate their Cobb angles.In light of the occlusion of other organs and complex background interference in orthopaedic Xray images,a neural network model based on the embedded attention mechanism,vector loss module,and vertebrafocused landmark detection(VFLD)network is proposed.The rotary attention mechanism module is embedded between the encoder and decoder to enhance the network’s extraction of the deep and highdimensional features of the spine bone,inhibit the interference of other organs,and allow the use of the vector similarity loss function to train the network.The experimental results show that the accuracy of the symmetrical mean absolute percentage error of the proposed model in the MICCAI 2019 open spine challenge dataset is as high as 9.31,and can effectively improve the ability of the original model to detect vertebral corners.Compared with many existing models,the proposed model has a higher accuracy and robustness.