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Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models

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Information Processing in Medical Imaging (IPMI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7917))

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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.

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  1. 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)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D.: Hierarchical, learning-based automatic liver segmentation. In: CVPR (2008)

    Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. 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)

    Chapter  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. 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)

    Google Scholar 

  8. Liu, D., Zhou, S.K., Bernhardt, D., Comaniciu, D.: Search strategies for multiple landmark detection by submodular maximization. In: CVPR. IEEE (2010)

    Google Scholar 

  9. Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in ct volumes. In: MICCAI-PMMIA Workshop (2009)

    Google Scholar 

  10. 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)

    Chapter  Google Scholar 

  11. 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)

    Chapter  Google Scholar 

  12. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)

    Google Scholar 

  13. Friedman, J., Hastie, T., Tibshirani, R.: The elements of statistical learning. Springer Series in Statistics, vol. 1 (2001)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

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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.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38867-5

  • Online ISBN: 978-3-642-38868-2

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