Abstract
We propose a novel framework for rapid and accurate segmentation of a cohort of organs. First, it integrates local and global image context through a product rule to simultaneously detect multiple landmarks on the target organs. The global posterior integrates evidence over all volume patches, while the local image context is modeled with a local discriminative classifier. Through non-parametric modeling of the global posterior, it exploits sparsity in the global context for efficient detection. The complete surface of the target organs is then inferred by robust alignment of a shape model to the resulting landmarks and finally deformed using discriminative boundary detectors. Using our approach, we demonstrate efficient detection and accurate segmentation of liver, kidneys, heart, and lungs in challenging low-resolution MR data in less than one second, and of prostate, bladder, rectum, and femoral heads in CT scans, in roughly one to three seconds and in both cases with accuracy fairly close to inter-user variability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yang, J., Duncan, J.S.: 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Medical Image Analysis 8(3), 285–294 (2004)
Zheng, Y., Georgescu, B., Ling, H., Zhou, S.K., Scheuering, M., Comaniciu, D.: Constrained marginal space learning for efficient 3D anatomical structure detection in medical images. In: CVPR, pp. 194–201. IEEE (2009)
Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D.: Hierarchical, learning-based automatic liver segmentation. In: CVPR (2008)
Zhou, S.K.: Shape regression machine and efficient segmentation of left ventricle endocardium from 2D b-mode echocardiogram. Medical Image Analysis 14(4), 563–581 (2010)
Kohlberger, T., Sofka, M., Zhang, J., Birkbeck, N., Wetzl, J., Kaftan, J., Declerck, J., Zhou, S.K.: Automatic multi-organ segmentation using learning-based segmentation and level set optimization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 338–345. Springer, Heidelberg (2011)
Shimizu, A., Ohno, R., Ikegami, T., Kobatake, H., Nawano, S., Smutek, D.: Segmentation of multiple organs in non-contrast 3D abdominal CT images. International Journal of Computer Assisted Radiology and Surgery 2, 135–142 (2007)
Sofka, M., Zhang, J., Zhou, S.K., Comaniciu, D.: Multiple object detection by sequential Monte Carlo and hierarchical detection network. In: CVPR, June 13-18 (2010)
Liu, D., Zhou, S.K., Bernhardt, D., Comaniciu, D.: Search strategies for multiple landmark detection by submodular maximization. In: CVPR. IEEE (2010)
Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in ct volumes. In: MICCAI-PMMIA Workshop (2009)
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010 Workshop MVC. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R.: Automatic detection and segmentation of kidneys in 3D CT images using random forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 66–74. Springer, Heidelberg (2012)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)
Friedman, J., Hastie, T., Tibshirani, R.: The elements of statistical learning. Springer Series in Statistics, vol. 1 (2001)
Datar, M., Indyk, P.: Locality-sensitive hashing scheme based on p-stable distributions. In: SCG 2004: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM Press (2004)
Dasgupta, S., Freund, Y.: Random projection trees and low dimensional manifolds. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, STOC 2008, pp. 537–546. ACM, New York (2008)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
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
Lay, N., Birkbeck, N., Zhang, J., Zhou, S.K. (2013). Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-38868-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38867-5
Online ISBN: 978-3-642-38868-2
eBook Packages: Computer ScienceComputer Science (R0)