Naoki Kamiya PhD, Presenter: Nothing to Disclose

Xiangrong Zhou PhD, Abstract Co-Author: Nothing to Disclose

Jun Kondo, Abstract Co-Author: Nothing to Disclose

Chisako Muramatsu PhD, Abstract Co-Author: Nothing to Disclose

Takeshi Hara PhD, Abstract Co-Author: Nothing to Disclose

Hiroshi Fujita PhD, Abstract Co-Author: Nothing to Disclose

Huayue Chen, Abstract Co-Author: Nothing to Disclose

Ryujiro Yokoyama, Abstract Co-Author: Nothing to Disclose

Masayuki Kanematsu MD, Abstract Co-Author: Consultant, DAIICHI SANKYO Group

Hiroaki Hoshi MD, Abstract Co-Author: Nothing to Disclose

In Japan, the proportion of elderly people has been increasing. The volume of the skeletal muscle is an important issue for the elderly. Particularly, psoas major muscle and abdominal wall muscle is a major role on walking ability and posture. In addition, torso CT images are widely used for diagnosis. However, it has to utilize all the information of the image is difficult for physicians. So, in diagnosis, not all the images obtained by torso CT are used. In this research, we develop a system to automatically analyze the volume of the multiple skeletal muscles, which are not focused on the diagnosis in the CT images. Our target is the deep muscle and surface muscle in the abdominal region. The former is the psoas major muscle and the latter is rectus abdominis muscle and abdominal wall muscles.

Our method consists of two parts. One is the construction of the shape models in each muscle and another is automatic recognition of it by use of the model. We represented the skeletal muscle using three features. First, to recognize the location of the origin and insertion as landmarks. Second, to generate muscle fiber as centerlines. Finally, the shape model is constructed by statistical analysis of the each skeletal muscle that segmented by the physician manually. Then, using of mathematical functions approximates each muscles outer shape. We applied proposed method to 50 adult normal CT cases.

We generated the muscular shape model of the normal adult patient. Then, also evaluate the recognition accuracy of the muscular volume by use of the generated shape model. The average values of the recognition result are 84% in the rectus abdominis, 82% in the lateroabdominal muscle and 87% in the psoas major muscle.

A fully automated approach for quantitative measurement of multiple skeletal muscle based on the statistical shape model generation is developed. This technique can help doctors to identify multiple muscles and to understand its condition easily in the abdominal region.

To quantify the volume and thickness of the skeletal muscles for diagnosis supports. To estimate the normal distribution of muscles as the basic data for the further clinic purpose.

Kamiya, N, Zhou, X, Kondo, J, Muramatsu, C, Hara, T, Fujita, H, Chen, H, Yokoyama, R, Kanematsu, M, Hoshi, H, Fully Automated Quantitative Measurement of Multiple Skeletal Muscles in Torso CT Images by Use of a Statistical Shape Models.  Radiological Society of North America 2012 Scientific Assembly and Annual Meeting, November 25 - November 30, 2012 ,Chicago IL. http://archive.rsna.org/2012/12023860.html