基于生成对抗网络的下肢X光图像三维重建算法  

3D Reconstruction Algorithm for Lower Limb X-ray Images Based on Generative Adversarial Networks

作　　者：叶瑞雯 王宝会[1] YE Ruiwen;WANG Baohui(School of Software,Beihang University,Beijing 100083,China)

机构地区：[1]北京航空航天大学软件学院,北京100083

出　　处：《计算机科学》2024年第S02期222-228,共7页Computer Science

基　　金：北京市自然科学基金(L222059)。

摘　　要：下肢骨畸形一直是骨科医疗中一个常见且治疗难度较大的病症,通常需要医生基于病人下肢骨正侧位X光片进行畸形程度判断。其诊断与手术方案设计高度依赖医生的专业程度与经验水平,是当前医疗领域非常重要的一个难题。为了降低医生诊断难度,需要给医生提供更加直观准确的下肢骨畸形模型展示。文中将人工智能深度学习技术应用到医疗影像处理与三维重建中,提出PSSobel-X2CTGAN模型以实现基于二维X光影片到三维CT图像的重建。主要研究内容包括:1)调研梳理CT影像归一化、裁剪缩放和DRR生成的数据预处理流程,使其能更好地应用于三维重建模型的训练和预测;2)将生成对抗原理运用于模型训练中,通过对生成器上采样过程的优化使得生成的三维模型更加接近真实情况;3)设计合理的损失函数,在基本的重建损失和投影损失基础上,引入sobel loss使得最终图片的边缘更加清晰,更适用于高精度的三维骨模型重建。在开源的盆骨和膝关节数据上进行实验,结果表明所提模型在各项评价指标上都优于原始模型,且从可视化的图片结果来看,该模型所提能取得较为满意的效果,对下肢畸形诊断具有实用价值。Lower limb bone deformities have always been a common and difficult to treat disease in orthopedic medicine.Doctors usually need to judge the degree of deformities based on the patient’s lower limb bone anteroposterior and lateral X-ray images.Its diagnosis and surgical plan design highly rely on the professional level and experience level of doctors,which is a very important challenge in the current medical field.In order to reduce the difficulty of diagnosis for doctors and provide them with more intuitive and accurate models of lower limb bone deformities,this study applies artificial intelligence deep learning technology to medical image processing and 3D reconstruction,and proposes the PSSobel-X2CTGAN model to achieve reconstruction from 2D X-ray films to 3D CT images.The main research content includes:1)investigating and organizing the data preprocessing processes of CT image normalization,cropping and scaling,and DRR generation,so as to better apply them to the training and prediction of 3D reconstruction models;2)apply the generative adversarial principle to model training,and optimize the sampling process on the generator to make the generated 3D model closer to the real situation;3)design a reasonable loss function,based on the basic reconstruction and projection losses,and introduce sobel loss to make the edges of the final image clearer,making it more suitable for high-precision 3D bone model reconstruction.Experiments are conducted on open-source pelvic and knee joint data,and the results showed that this model outperformed the original model in various evaluation indicators.Moreover,from the visual image results,the model can achieve satisfactory results and has practical value for the diagnosis of lower limb deformities.

关 键 词：下肢畸形矫正 三维重建 医学影像处理 生成对抗网络 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]