Abstract
Our motivation was to provide an automatic tool for radiologists and orthopedic surgeons for improving the quality of life of an aging population. We propose a method for generating a shape model and a fully automated segmenting scheme for the psoas major muscle in X-ray CT images by using the shape model. Our approach consists of two steps: (1) The generation of a shape model and its application to muscle segmentation. The shape model describes the muscle’s outer shape and has two parameters, an outer shape parameter and a fitting parameter. The former was determined by approximating of the outer shape of the muscle region in training cases. The latter was determined for each test case in the recognition process. (2) Finally, the psoas major muscle was segmented by use of the shape model. To evaluate the performance of the method, we applied it to CT images for constructing the shape models by using 20 cases as training samples; 80 cases were used for testing. The accuracy of this method was measured by comparison of the extracted muscle regions with regions that were identified manually by an expert radiologist. The experimental results of the segmentation of the psoas major muscle gave a mean Jaccard similarity coefficient of 72.3%. The mean true segmentation coefficient was 76.2%. The proposed method can be used for the analysis of cross-sectional area and muscular thickness in a transverse section, offering radiologists an alternative to manual measurement for saving their time and improving the reproducibility of segmentation.
Similar content being viewed by others
References
Cabinet Office. Annual Report on the Aging Society 2006, Available: http://www8.cao.go.jp/kourei/english/annualreport/2006/06wp-e.html.
Sturnieks DL, George R, Lord SR. Balance disorders in the elderly. Neurophysiol Clin. 2008;38:467–78.
Takahashi K, Takahashi HE, Nakadaira H, Yamamoto M. Different changes of quantity due to aging in the psoas major and quadriceps femoris muscles in women. J Musculoskelet Neuronal Interact. 2006;6:201–5.
Hohne KH, Pflesser B, Pommert A, Riemer M, Schubert R, Schiemann T, Tiede U, Schumacher U. A realistic model of human structure from the visible human data. Methods Inf Med. 2001;40:83–9.
Qin Y, Cheng Z, Zhuang T, Wang H, Wang Y, Yan Z, Tiede RM, Hohne U. Interactive segmentation of muscles and 3D representation of meridians based on visible human. Conf Proc IEEE Eng Med Biol Soc. 2005;5:5116–9.
Gilles B, Moccozet L, Thalmann NM. Anatomical modelling of the musculoskeletal system from MRI. Med Image Comput Comput Assist Interv. 2006;9:289–96.
Ng HP, Hu QM, Ong SH, Foong KWC, Goh PS, Liu J, Nowinski L. Segmentation of the temporalis muscle from MR data. Int J Comput Assist Radiol Surg. 2007;2:19–30.
Zhou X, Hayashi T, Han M, Chen H, Hara T, Fujita H, Yokoyama R, Kanematsu M, Hoshi H. Automated segmentation and recognition of the bone structure in non-contrast torso CT images using implicit anatomical knowledge. Proc SPIE, 2009;7259, 72593S. doi:10.1117/12.812945.
Zhou X, Hayashi T, Hara T, Fujita H, Yokoyama R, Kiryu T, Hoshi H. Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images. Comput Med Imaging Graph. 2006;30(5):299–313.
Zhou X, Kamiya N, Hara T, Fujita H, Chen H, Yokoyama R, Hoshi H. Automated segmentation and recognition of abdominal wall muscles in X-ray torso CT images and its application in abdominal CAD. Int J Comput Assist Radiol Surg. 2007;2:389–90.
Anderberg MR. Cluster analysis for applications. Academic press; 1973.
Hori D, Katsuragawa S, Murakami R, Hirai T. Semi-automated segmentation of glioblastoma multiforme on brain MR images for radiotherapy planning. Jpn J Radiol Technol. 2010;66(4):353–62.
Kernan WN, Viscoli CM, Makuch RW, Brass LM, Horwitz Rl. Stratified randomization for clinical trials. J Clin Epidemiol. 1999;52:19–26.
Drake R, Vogl W, Mitchell AWM. Gray’s anatomy for students. Churchill Livingstone; 2005.
Saito T, Toriwaki J. New algorithms for Euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognit. 1994;27(11):1551–65.
Gonzalez RC, Woods RE. Digital image processing, 2nd ed. Prentice Hall; 2002.
Kojima S, Zhou X, Hara T, Fujita H, Azuma K, Yokoyama R, Hoshi H. Automated analysis of bone structures for anatomical structure recognitions on torso X-ray CT images. IEICE Tech Report (in Japanese). 2006;106(343):43–8.
Otsu N. A threshold selection method from gray-level histograms. IEEE Syst Man Cybern. 1979;9(1):62–6.
Acknowledgments
We thank all the members of the Fujita Laboratory of Gifu University for their valuable contributions to this work. This research was funded in part by a Grant-in-Aid for Scientific Research on Innovative Areas, MEXT, Japan, in part by the Ministry of Health, Labour, and Welfare under a Grant-in-Aid for Cancer Research, Japan, and also in part by the Ministry of Health, Labour, and Welfare under a Grant-in-Aid for Cancer Research, Japan.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Kamiya, N., Zhou, X., Chen, H. et al. Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol Phys Technol 5, 5–14 (2012). https://doi.org/10.1007/s12194-011-0127-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12194-011-0127-0