In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NMAR), which is one of the state-of-the-art metal artifact reduction methods, and a Convolutional Neural Network-based segmentation using two U-net architectures. The first U-net refines the result of NMAR and the Bayesian muscle segmentation is performed by the second U-net. We conducted experiments using simulated images of 20 patients and real images of three patients to evaluate the segmentation accuracy of 19 muscles. In simulation study, the proposed method showed statistically significant improvement (p < 0.05) in the average symmetric surface distance (ASD) metric for 12 muscles out of 19 muscles and the average ASD of all muscles from 1.46 ± 0.904 mm (mean ± std. over all patients) to 1.30 ± 0.775 mm over our previous method. Addition to this, the high correlation ratio between segmentation accuracy and the estimated uncertainty was found. The real image study using the manual trace of gluteus maximus and medius muscles showed ASD of 1.89 ± 0.553 mm.

在全髋关节置换术中，分析术后医学图像对于评估手术效果非常重要。由于计算机断层扫描（CT）是整形外科中最普遍的一种方式，因此我们旨在分析CT图像。在这项工作中，我们重点研究由金属植入物引起的术后CT中的金属假象，这会降低分割的准确性，尤其是在植入物附近。我们的目标是开发一种在术后CT图像中自动分割骨骼和肌肉的方法。我们提出了一种方法，该方法将归一化金属伪影减少（NMAR）（一种最先进的金属伪影减少方法）与使用两个U-net架构的基于卷积神经网络的分割相结合。第一个U网络细化NMAR的结果，第二个U网络进行贝叶斯肌肉分割。我们使用20位患者的模拟图像和3位患者的真实图像进行了实验，以评估19条肌肉的分割准确性。在仿真研究中，所提出的方法显示出统计学上的显着改进（p  <0.05），其中19块肌肉中的12块肌肉的平均对称表面距离（ASD）指标以及所有肌肉的平均ASD从1.46±0.904 mm（所有患者的平均值±标准）到我们以前的1.30±0.775 mm方法。除此之外，发现分割精度和估计的不确定性之间的高相关比。使用臀大肌和臀中肌的手动迹线进行的真实图像研究显示，ASD为1.89±0.553 mm。