Abstract
Current methods for abdominal multi-organ segmentation (MOS) in CT can fail to handle clinical patient population with missing organs due to surgical removal. In order to enable the state-of-the-art atlas-guided MOS for these clinical cases, we propose 1) statistical organ location models of 10 abdominal organs, 2) organ shift models that capture organ shifts due to specific surgical procedures, and 3) data-driven algorithms to detect missing organs by using a normality test of organ centers and a texture difference in intensity entropy. The proposed methods are validated with 34 contrast-enhanced abdominal CT scans, resulting in 80% detection rate at 15% false positive rate for missing organ detection. Additionally, the method allows the detection/segmentation of abdominal organs from difficult diseased cases with missing organs.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stats. Soc. Series B 39, 1–38 (1977)
Guld, M.O., Kohnen, M., Keysers, D., Schubert, H., Wein, B.B., Bredno, J., Lehmann, T.M.: Quality of DICOM header information for image categorization. In: SPIE, vol. 4685, pp. 280–287 (2002)
Kobashi, M., Shapiro, L.G.: Knowledge-based organ identification from CT images. Pattern Recognition 28, 475–491 (1995)
Kobatake, H.: Future CAD in multi-dimensional medical images - project on multi-organ, multi-disease CAD system. Computerized Medical Imaging and Graphics 31, 258–266 (2007)
Lee, C.C., Chung, P.C., Tsai, H.M.: Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules. IEEE Trans. Info. Tech. in Biomed. 7, 208–217 (2003)
Linguraru, M.G., Pura, J.A., Chowdhury, A.S., Summers, R.M.: Multi-Organ Segmentation from Multi-Phase Abdominal CT via 4D Graphs using Enhancement, Shape and Location Optimization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 89–96. Springer, Heidelberg (2010)
Linguraru, M.G., Sandberg, J.K., Li, Z., Pura, J.A., Summers, R.M.: Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation. Medical Physics 37, 771–783 (2010)
Liu, X., Linguraru, M.G., Yao, J., Summers, R.M.: Organ pose distribution model and an MAP framework for automated abdominal multi-organ localization. In: Proc. Medical Imaging and Augmented Reality, pp. 393–402 (2011)
Okada, T., Yokota, K., Hori, M., Nakamoto, M., Nakamura, H., Sato, Y.: Construction of Hierarchical Multi-Organ Statistical Atlases and their Application to Multi-Organ Segmentation from CT Images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 502–509. Springer, Heidelberg (2008)
Park, H., Bland, P.H., Meyer, C.R.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans. Medical Imaging 22, 483–492 (2003)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Medical Imaging 18, 712–721 (1999)
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: Proc. SPIE Conf. Medical Imaging (2008)
Shimizu, A., Ohno, R., Ikegami, T., Kobatake, H., Nawano, S., Smutek, D.: Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int. J. CARS 2, 135–143 (2007)
Yao, J., Summers, R.M.: Statistical Location Model for Abdominal Organ Localization. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 9–17. Springer, Heidelberg (2009)
Zhou, Y., Bai, J.: Multiple abdominal organ segmentation: An atlas-based fuzzy connectedness approach. IEEE Trans. Info. Tech. in Biomed. 11, 348–352 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suzuki, M., Linguraru, M.G., Summers, R.M., Okada, K. (2012). Analyses of Missing Organs in Abdominal Multi-Organ Segmentation. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-28557-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28556-1
Online ISBN: 978-3-642-28557-8
eBook Packages: Computer ScienceComputer Science (R0)