Automated segmentation of sternocleidomastoid muscle using atlas-based method in X-ray CT images: Preliminary study

Abstract

Sternocleidomastoid muscle is the biggest skeletal one in neck region and has a medical significance for evaluating the influence of Amyotrophic lateral sclerosis（ALS）. Since the morphological change of the muscle is often associated with ALS, the precise measurement of volume and density for the muscle is important for the early and quantitative diagnosis. The purpose of this study was to evaluate the initial results of automatic segmentation for the sternocleidomastoid muscle in whole-body and torso CT images. We construct a probabilistic atlas for the sternocleidomastoid muscle without any abnormalities. The procedure to construct the atlas was based on the technique developed for internal organs. The muscle shape for the atlas was created by manual procedures, and used as gold standards for the evaluation of segmented results. The probabilistic atlas was aligned with each individual muscle on the basis of the bone anatomical location and the edge of the muscle. We used 10 cases of whole-body CT images with abnormalities in the skeletal muscles, and 20 cases of torso CT images with no abnormalities in the skeletal muscles. As a result, the average concordance rates of sternocleidomastoid muscle were 60.3% and 65.4%, respectively. We successfully segmented the major area of the sternocleidomastoid muscle. This is because the atlas of sternocleidomastoid muscle deformed using the information of bone anatomical location and edge of the sternocleidomastoid muscle is fitted in the shape of the individual muscle.