Masseter Muscle Segmentation from Cone-Beam CT Images using Generative Adversarial Network

Abstract

Masseter segmentation from noisy and blurry cone-beam CT (CBCT) images is a challenging issue considering the device-specific image artefacts. In this paper, we propose a novel approach for noise reduction and masseter muscle segmentation from CBCT images using a generative adversarial network (GAN)-based framework. We adapt the regression model of muscle segmentation from traditional CT (TCT) images to the domain of CBCT images without using prior paired images. The proposed framework is built upon the unsupervised CycleGAN. We mainly address the shape distortion problem in the unsupervised domain adaptation framework. A structure-aware constraint is introduced to guarantee the shape preservation in the feature embedding and image generation processes. We explicitly define a joint embedding space of both the TCT and CBCT images to exploit the intrinsic semantic representation, which is key to the intra-and cross-domain image generation and muscle segmentation. The proposed approach is applied to clinically captured CBCT images. We demonstrate both the effectiveness and efficiency of the proposed approach in noise reduction and muscle segmentation tasks compared with the state-of-the-art.