Abstract
The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.
Chapter PDF
Similar content being viewed by others
References
Wolz, R., Chu, C., Misawa, K., Mori, K., Rueckert, D.: Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 10–17. Springer, Heidelberg (2012)
Seifert, S., Barbu, A., Zhou, S.K., Liu, D., Feulner, J., Huber, M., Suehling, M., Cavallaro, A., Comaniciu, D.: Hierarchical parsing and semantic navigation of full body CT data. In: Pluim, J.P.W., Dawant, B.M. (eds.) Medical Imaging 2009: Image Proceedings, SPIE, vol. 7259, p. 725902 (2009)
Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled decision forests and their application for semantic segmentation of CT images. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 184–196. Springer, Heidelberg (2011)
Linguraru, M.G., Pura, J.A., Pamulapati, V., Summers, R.M.: Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT. Med. Image Anal. 16(4), 904–914 (2012)
Okada, T., Linguraru, M.G., Hori, M., Suzuki, Y., Summers, R.M., Tomiyama, N., Sato, Y.: Multi-organ segmentation in abdominal CT images. In: 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, pp. 3986–3989 (2012)
Shimizu, A., Kimoto, T., Kobatake, H., Nawano, S., Shinozaki, K.: Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int. J. Comput. Assist. Radiol. Surg. 5(1), 85–98 (2010)
Freiman, M., Kronman, A., Esses, S.J., Joskowicz, L., Sosna, J.: Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 73–80. Springer, Heidelberg (2010)
Okada, T., Shimada, R., Hori, M., Nakamoto, M., Chen, Y.W., Nakamura, H., Sato, Y.: Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model. Acad. Radiol. 15(11), 1390–1403 (2008)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Eighth IEEE International Conference on Computer Vision, pp. 105–112 (2001)
Rao, A., Aljabar, P., Rueckert, D.: Hierarchical statistical shape analysis and prediction of sub-cortical brain structures. Med. Image Anal. 12(1), 55–68 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Okada, T., Linguraru, M.G., Hori, M., Summers, R.M., Tomiyama, N., Sato, Y. (2013). Abdominal Multi-organ CT Segmentation Using Organ Correlation Graph and Prediction-Based Shape and Location Priors. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)