Abstract
The assessment of anomalies in the scoliotic spine using Magnetic Resonance Imaging (MRI) is an essential task during the planning phase of a patient’s treatment and operations. Due to the pathologic bending of the spine, this is an extremely time consuming process as an orthogonal view onto every vertebra is required. In this article we present a system for computer-aided assessment (CAA) of anomalies in 3-D MRI images of the spine relying on curved planar reformations (CPR). We introduce all necessary steps, from the pre-processing of the data to the visualization component. As the core part of the framework is based on a segmentation of the spinal cord we focus on this. The proposed segmentation method is an iterative process. In every iteration the segmentation is updated by an energy based scheme derived from Markov random field (MRF) theory. We evaluate the segmentation results on public available clinical relevant 3-D MRI data sets of scoliosis patients. In order to assess the quality of the segmentation we use the angle between automatically computed planes through the vertebra and planes estimated by medical experts. This results in a mean angle difference of less than six degrees.
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Lewonowski, K., King, J.D., Nelson, M.D.: Routine use of magnetic resonance imaging in idiopathic scoliosis patients less than eleven years of age. Spine 17(Suppl. 6), 109–116 (1992)
Gupta, P., Lenke, L.G., Bridwell, K.H.: Incidence of neural axis abnormalities in infantile and juvenile patients with spinal deformity. is a magnetic resonance screening necessary? Spine 23, 206–210 (1998)
Peng, Z., Zhong, J., Wee, W., Lee, J.: Automated vertebra detection and segmentation from the whole spine MR images. In: IEEE-EMBS, New York City, USA, pp. 2527–2530. IEEE, Los Alamitos (2006)
McIntosh, C., Hamarneh, G.: Spinal crawlers: Deformable organisms for spinal cord segmentation and analysis. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 808–815. Springer, Heidelberg (2006)
Coulon, O., Hickman, S.J., Barker, G.J., Miller, D.H., Arridge, S.R.: Quantification of spinal cord atrophy from magnetic resonance images via a b-spline active surface model. Magn. Reson. Med. 47(6), 1176–1185 (2002)
Brinkmann, B.H., Manduca, A., Robb, R.A.: Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Trans. Med. Imag. 17(2), 161–171 (1998)
Jäger, F., Hornegger, J.: Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging. IEEE Trans. Med. Imag. 28(1), 137–150 (2009)
Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-d medial surface/axis thinning algorithms. CVGIP 56(6), 462–478 (1994)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Computer Science Workbench. Springer, Tokyo (2001)
Fetita, C.L., Prêteux, F.J.: Quantitative 3d ct bronchography. In: ISBI, Washington, DC, USA, pp. 221–224. IEEE, Los Alamitos (2002)
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Jäger, F., Hornegger, J., Schwab, S., Janka, R. (2009). Computer-Aided Assessment of Anomalies in the Scoliotic Spine in 3-D MRI Images. 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_99
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DOI: https://doi.org/10.1007/978-3-642-04271-3_99
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