Abstract
Initial placement of the models is an essential pre-processing step for model-based organ segmentation. Based on the observation that organs move along with the spine and their relative locations remain relatively stable, we built a statistical location model (SLM) and applied it to abdominal organ localization. The model is a point distribution model which learns the pattern of variability of organ locations relative to the spinal column from a training set of normal individuals. The localization is achieved in three stages: spine alignment, model optimization and location refinement. The SLM is optimized through maximum a posteriori estimation of a probabilistic density model constructed for each organ. Our model includes five organs: liver, left kidney, right kidney, spleen and pancreas. We validated our method on 12 abdominal CTs using leave-one-out experiments. The SLM enabled reduction in the overall localization error from 62.0±28.5 mm to 5.8±1.5 mm. Experiments showed that the SLM was robust to the reference model selection.
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision, 321–331 (1988)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: IEEE International Conference on Computer Vision, Cambridge, MA USA (1995)
Cootes, T.F., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Park, H., Bland, P.H., Meyer, C.R.: Construction of an Abdominal Probabilistic Atlas and its Application in Segmentation. IEEE Trans. Med. Imag. 22(4), 483–492 (2003)
Okada, T., Yokota, K., Masatoshi, H., et al.: 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)
Shimizu, A., Ohno, R., Ikegami, T., Kobatake, H., et al.: Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int. J. CARS (2), 135–142 (2007)
Kaneko, T., Gu, L., Fujimoto, H.: Abdominal organ recognition using 3D mathematical morphology. In: Proc. of 15th Int Conf. on Pattern Recognition, Barcelona (2000)
Yao, J., O’Connor, S.D., Summers, R.M.: Automated Spinal Column Extraction and Partitioning. In: IEEE ISBI, Arlington, VA (2006)
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Yao, J., Summers, R.M. (2009). Statistical Location Model for Abdominal Organ Localization. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04271-3_2
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DOI: https://doi.org/10.1007/978-3-642-04271-3_2
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