Abstract
Segmenting tubular structures from medical image data is a common problem; be it vessels, airways, or nervous tissue like the spinal cord. Many application-specific segmentation techniques have been proposed in the literature, but only few of them are fully automatic and even fewer approaches maintain a convex formulation. In this paper, we show how to integrate a cross-sectional similarity prior into the convex continuous max-flow framework that helps to guide segmentations in image regions suffering from noise or artefacts. Furthermore, we propose a scheme to explicitly include tubularity features in the segmentation process for increased robustness and measurement repeatability. We demonstrate the performance of our approach by automatically segmenting the cervical spinal cord in magnetic resonance images, by reconstructing its surface, and acquiring volume measurements.
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References
Losseff, N.A., Webb, S.L., O’Riordan, J.I., Page, R., Wang, L., Barker, G.J., Tofts, P.S., McDonald, W.I., Miller, D.H., Thompson, A.J.: Spinal cord atrophy and disability in multiple sclerosis. Brain 119(3), 701–708 (1996)
Rashid, W., Davies, G.R., Chard, D.T., Griffin, C.M., Altmann, D.R., Gordon, R., Thompson, A.J., Miller, D.H.: Increasing cord atrophy in early relapsing-remitting multiple sclerosis: a 3 year study. J. Neurol., Neurosurg. Psychiatry 77(1), 51–55 (2006)
Asman, A., Smith, S., Reich, D., Landman, B.: Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013. Lecture Notes in Computer Science, vol. 8149, pp. 759–767. Springer, Heidelberg (2013)
De Leener, B., Kadoury, S., Cohen-Adad, J.: Robust, accurate and fast automatic segmentation of the spinal cord. NeuroImage 98, 528–536 (2014)
Miller, D.H., Barkhof, F., Frank, J.A., Parker, G.J.M., Thompson, A.J.: Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 125(8), 1676–1695 (2002)
Yuan, J., Bae, E., Tai, X.C.: A study on continuous max-flow and min-cut approaches. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2217–2224 (2010)
Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.: Fast globally optimal segmentation of 3d prostate mri with axial symmetry prior. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013. Lecture Notes in Computer Science, vol. 8150, pp. 198–205. Springer, Heidelberg (2013)
Tustison, N., Avants, B., Cook, P., Zheng, Y., Egan, A., Yushkevich, P., Gee, J.: N4ITK: improved n3 bias correction. IEEE TMI 29(6), 1310–1320 (2010)
Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale vessel enhancement filtering. In: Wells, W., Colchester, A., Delp, S. (eds.) Medical Image Computing and Computer-Assisted Interventation—MICCAI’98. Lecture Notes in Computer Science, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Yuan, J., Bae, E., Tai, X.C., Boykov, Y.: A study on continuous max-flow and min-cut approaches. Tech. Rep. CAM 10–61, UCLA, CAM, UCLA (2010)
Janke, A., Zhao, H., Cowin, G.J., Galloway, G.J., Doddrell, D.M.: Use of spherical harmonic deconvolution methods to compensate for nonlinear gradient effects on MRI images. Magn. Reson. Med. 52(1), 115–122 (2004)
Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. Proceedings, vol. 1, pp. 105–112 (2001)
Pezold, S., Amann, M., Weier, K., Fundana, K., Radue, E., Sprenger, T., Cattin, P.: A semi-automatic method for the quantification of spinal cord atrophy. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging, Lecture Notes in Computational Vision and Biomechanics, vol. 17, pp. 143–155. Springer International Publishing (2014)
Acknowledgments
We would like to thank Ernst-Wilhelm Radue and the MIAC AG, University Hospital Basel, Basel, Switzerland, for their support.
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Pezold, S. et al. (2015). Automatic Segmentation of the Spinal Cord Using Continuous Max Flow with Cross-sectional Similarity Prior and Tubularity Features. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-14148-0_10
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DOI: https://doi.org/10.1007/978-3-319-14148-0_10
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