Bayesian Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT Using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction

Abstract

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.