Abstract
We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10–100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17–2.89mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.
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Chan, T.F., Vese, L.A.: Active Contours without Edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)
Cremers, D., Rousson, M., Deriche, R.: A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape. IJCV 72(2), 195–215 (2007)
Kohlberger, T., Uzunbas, G., Alvino, C., Kadir, T., Slosman, D., Funka-Lea, G.: Organ Segmentation with Level Sets Using Local Shape and Appearance Priors. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 34–42. Springer, Heidelberg (2009)
Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D.: Hierarchical, Learning-based Automatic Liver Segmentation. In: CVPR 2008, pp. 1–8. IEEE Press, New York (2008)
Liu, D., Zhou, S.K., Bernhardt, D., Comaniciu, D.: Search Strategies for Multiple Landmark Detection by Submodular Maximization. In: CVPR 2010, pp. 2831–2838. IEEE Press, New York (2010)
Mansouri, A.-R., Mitiche, A., Vázquez, C.: Multiregion Competition: A Level Set Extension of Region Competition to Multiple Region Image Partitioning. Computer Vision and Image Understanding 101(3), 137–150 (2005)
Paragios, N.: A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis. IJCV 50(3), 345–362 (2002)
Rousson, M., Paragios, N.: Shape Priors for Level Set Representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)
Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: ICCV 2005, vol. 2, pp. 1589–1596. IEEE Press, New York (2005)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features. IEEE Trans. on Medical Imaging 27(11), 1668–1681 (2008)
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Kohlberger, T. et al. (2011). Automatic Multi-organ Segmentation Using Learning-Based Segmentation and Level Set Optimization. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_42
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DOI: https://doi.org/10.1007/978-3-642-23626-6_42
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