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Background: An abnormal sternocleidomastoid muscle in congenital muscular torticollis can be classified into one of the four types via sonography. However, this categorization lacks quantitative measurements. The purpose of the study was to determine quantitative measurements of the sonograms via image analysis. Methods: Infants younger than 12 months of age suspected of having congenital muscular torticollis were included. Intraclass correlation coefficient estimates for interobserver reliability and a simple regression analysis for criterion validity were calculated. Spearman correlation analysis was then performed. The analyzed parameters included cervical passive range of motion for lateral flexion and rotation, area, brightness, max/min Feret's diameters, and muscular width/thickness. Results: Of the 29 (4.0 ± 2.6 months) screened infants, 13 (1.9 ± 1.7 months) were included. Nine were male, and 4 were female. Seven infants with mass were ultrasonographically classified into type I, and the other six infants were classified into type II. The affected/unaffected side ratios of cervical passive range of motion for lateral flexion and rotation were 0.92 ± 0.13 and 0.88 ± 0.16, respectively. The parameters measured on the sonograms were reliable, and the max/min Feret's diameters were valid measurements. The affected/unaffected side ratio of cervical passive range of motion for rotation significantly correlated with the affected/unaffected side ratios of the sternocleidomastoid muscle sonogram on area (r = −0.62, p = 0.03) and min Feret's diameter (r = −0.69, p = 0.01). Conclusions: The area and min Feret's diameter were efficacious parameters for image analysis on sternocleidomastoid sonograms, and the min Feret's diameter would be more suitable than thickness for measuring the thickening SCM in transverse view. A healthy control group, more data and follow-up would be needed to confirm the changes on the SCM sonograms for clinical decision. Keywords: Congenital muscular torticollis, Sternocleidomastoid, Sonography, Cervical passive range of motion

Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual−real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual−real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual−real registration to AR in outdoor environments.