Abstract
We propose a novel segmentation method based on higher-order graph cuts which enables the utilization of prior knowledge regarding anatomical shapes. We applied the method for segmentation of psoas major muscles by using combinations of logistic curves which representing their shapes. The higher-order terms consisting of variables (voxels) just inside or outside of the estimated shapes are added to the energy function to encourage the segmentation results to fit to the shapes. We verified the effectiveness of the method with 20 abdominal CT images. By comparing the segmentation results to the ground truth data prepared by a clinical expert, we validated the method where it achieved the Jaccard similarity coefficient (JSC) of 75.4 % (right major) and 77.5 % (left major). We also confirmed that the proposed method worked well for thick CT images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Martin, L., Birdsell, L., Macdonald, N., Reiman, T., Clandinin, M.T., McCargar, L.J., Murphy, R., Ghosh, S., Sawyer, M.B., Baracos, V.E.: Cancer cachexia in the age of obesty: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J. Clin. Oncol. 31(12), 1539–1547 (2013)
Wannamethee, S.G., Shaper, A.G., Whincup, P.H., Lennon, L., Papacosta, O., Sattar, N.: The obesity paradox in men with coronary heart disease and heart failure: the role of muscle mass and leptin. Int. J. Cardiol. 171(1), 49–55 (2014)
Kamiya, N., Zhou, X., Chen, H., Muramatsu, C., Hara, T., Yokoyama, R., Kanematsu, M., Hoshi, H., Fujita, H.: Automated Segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol. Phys. Technol. 5(1), 5–14 (2012)
Meesters, S.P.L., Yokota, F., Okada, T., Takaya, M., Tomiyama, N., Yao, J., Liguraru, M.G., Summers, R.M., Sato, Y.: Multi atlas-based muscle segmentation in abdominal CT images with varying field of view. Paper presented at the International Forum on Medical Imaging in Asia (IFMIA), 16–17 November 2012
Kitamura, Y., Li, Y., Ito, W., Ishikawa, H.: Coronary lumen and plaque segmentation from CTA using higher-order shape prior. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 339–347. Springer, Heidelberg (2014)
Wang, C., Li, Y., Ito, W., Shimura, K., Abe, K.: A machine learning approach to extract spinal column centerline from three-dimensional CT data. In: Proceedings of SPIE, vol. 7259, p. 72594T (2009)
Kohli, P., Ladicky, L., Torr, P.H.S.: Robust higher order potentials for enforcing label consistency. In: Proceedings of CVPR (2008)
Kadoury, S., Abi-Jaoudeh, N., Valdes, P.A.: Higher-order CRF tumor segmentation with discriminant manifold potentials. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 719–726. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Inoue, T., Kitamura, Y., Li, Y., Ito, W., Ishikawa, H. (2016). Psoas Major Muscle Segmentation Using Higher-Order Shape Prior. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2015. Lecture Notes in Computer Science(), vol 9601. Springer, Cham. https://doi.org/10.1007/978-3-319-42016-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-42016-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42015-8
Online ISBN: 978-3-319-42016-5
eBook Packages: Computer ScienceComputer Science (R0)