Abstract
Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.
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Jenkins, J.P., Hickey, D.S., Zhu, X.P., Machin, M., Isherwood, I.: Mr imaging of the intervertebral disc: A quantitative study. British Journal of Radiology 58(692), 705–709 (1985)
Antoniou, J., Mwale, F., Demers, C.N., Beaudoin, G., Goswami, T., Aebi, M., Alini, M.: Quantitative magnetic resonance imaging of enzymatically induced degraded of the nucleus pulposus of inteverbetral discs. Spine 31(14), 1547–1554 (2006)
Dalley, A.F., Agur, A.M.R.: Atlas of Anatomy. Lippincott Williams and Wilkins (2004)
Chwialkowski, M.P., Shile, P.E., Peshock, R.M., Pfeifer, D., Parkey, R.W.: Automated detection and evaluation of lumbar discs in mr images. In: Proc. of IEEE EMBS (1989)
Peng, Z., Zhong, J., Wee, W., Lee, J.: Automated vertebra detection and segmentation from the whole spine MR images. In: Proc. of IEEE EMBS, vol. 3 (2005)
Pekar, V., Bystrov, D., Heese, H.S., Dries, S.P.M., Schmidt, S., Grewer, R., Harder, C., Bergmans, R.C., Simonetti, A.W., Muisinkel, A.: Automated planning of scan geometries in spine MRI scans. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 601–608. Springer, Heidelberg (2007)
Masaki, T., Lee, Y., Tsai, D.Y., Sekiya, M., Kazama, K.: Automatic detectmination of the imaging plane in lumbar mri. In: Proc. of SPIE Med. Img., pp. 1252–1259 (2006)
Weiss, K.L., Storrs, J.M., Banto, R.B.: Automated spine survey iterative scan technique. Radiology 239(1), 255–262 (2006)
Zheng, Y., Nixon, M.S., Allen, R.: Automatic segmentation of lumbar vertebrae in digital videofluoroscopic imaging. IEEE Trans. on Medical Imaging 23(1), 45–52 (2004)
Schmidt, S., Kappes, J., Bergtholdt, M., Pekar, V., Dries, S.P., Bystrov, D., Schnorr, C.: Spine Detection and Labeling Using a Parts-Based Graphical Model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (2007)
Fischl, B., Salat, D.H., Busa, E., Albert, M., Deiterich, M., Haselgrove, C., Kouwe, A.v.d., Killiany, R., Kennedy, D., Klaveness, S., Monttillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron. 33, 341–355 (2002)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood From Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society – Series B 39(1), 1–38 (1977)
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Corso, J.J., Alomari, R.S., Chaudhary, V. (2008). Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_25
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DOI: https://doi.org/10.1007/978-3-540-85988-8_25
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