Abstract
In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.
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Schmidt, S., Kappes, J.H., Bergtholdt, M., Pekar, V., Dries, S.P.M., Bystrov, D., Schnörr, 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)
Corso, J.J., Alomari, R.S., Chaudhary, V.: 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.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 202–210. Springer, Heidelberg (2008)
Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012)
Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013)
Chevrefils, C., Cheriet, F., Aubin, C.E., Grimard, G.: Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from mr images. IEEE Trans. on Information Technology in Biomedicine 13, 608–620 (2009)
Michopoulou, S.K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atalas-based segmentation of degenerated lumbar intervertebral discs from mr images of the spine. IEEE Trans. on Biomedical Engineering 56(9), 2225–2231 (2009)
Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph cuts with invariant object-interaction priors: Application to intervertebral disc segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011)
Neubert, A., Fripp, J., Shen, K., Salvado, O., Schwarz, R., Lauer, L., Engstrom, C., Crozier, S.: Automatic 3D segmentation of vertebral bodies and intervertebral discs from mri. In: International Conference on Ditial Imaging Computing: Techniques and Applications (2011)
Law, M.W.K., Tay, K., Leung, A., Garvin, G.J., Li, S.: Intervertebral disc segmentation in mr images using anisotropic oriented flux. Medical Image Analysis 17, 43–61 (2013)
Chen, C., Xie, W., Franke, J., Grutzner, P.A., Nolte, L.-P., Zheng, G.: Automatic x-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements. Medical Image Analysis 18, 487–499 (2014)
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Chen, C., Belavy, D., Zheng, G. (2014). 3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_7
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DOI: https://doi.org/10.1007/978-3-319-10581-9_7
Publisher Name: Springer, Cham
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