Abstract
Accurate localization and identification of vertebrae in spinal imaging is crucial for the clinical tasks of diagnosis, surgical planning, and post-operative assessment. The main difficulties for automatic methods arise from the frequent presence of abnormal spine curvature, small field of view, and image artifacts caused by surgical implants. Many previous methods rely on parametric models of appearance and shape whose performance can substantially degrade for pathological cases.
We propose a robust localization and identification algorithm which builds upon supervised classification forests and avoids an explicit parametric model of appearance. We overcome the tedious requirement for dense annotations by a semi-automatic labeling strategy. Sparse centroid annotations are transformed into dense probabilistic labels which capture the inherent identification uncertainty. Using the dense labels, we learn a discriminative centroid classifier based on local and contextual intensity features which is robust to typical characteristics of spinal pathologies and image artifacts. Extensive evaluation is performed on a challenging dataset of 224 spine CT scans of patients with varying pathologies including high-grade scoliosis, kyphosis, and presence of surgical implants. Additionally, we test our method on a heterogeneous dataset of another 200, mostly abdominal, CTs. Quantitative evaluation is carried out with respect to localization errors and identification rates, and compared to a recently proposed method. Our approach is efficient and outperforms state-of-the-art on pathological cases.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Ben Ayed, I., Punithakumar, K., Minhas, R., Joshi, R., Garvin, G.J.: Vertebral Body Segmentation in MRI via Convex Relaxation and Distribution Matching. In: MICCAI 2012, Part I. LNCS, vol. 7510, pp. 520–527. Springer, Heidelberg (2012)
Yao, J., Burns, J.E., Munoz, H., Summers, R.M.: Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 509–516. Springer, Heidelberg (2012)
Steger, S., Wesarg, S.: Automated Skeleton Based Multi-modal Deformable Registration of Head&Neck Datasets. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 66–73. Springer, Heidelberg (2012)
Lecron, F., Boisvert, J., Mahmoudi, S., Labelle, H., Benjelloun, M.: Fast 3D Spine Reconstruction of Postoperative Patients Using a Multilevel Statistical Model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 446–453. Springer, Heidelberg (2012)
Hsiang, J.: Wrong-level surgery: A unique problem in spine surgery. Surg. Neurol. Int. 2(47) (2011)
Ma, J., Lu, L., Zhan, Y., Zhou, X., Salganicoff, M., Krishnan, A.: Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 19–27. Springer, Heidelberg (2010)
Oktay, A.B., Akgul, Y.S.: Localization of the Lumbar discs using machine learning and exact probabilistic inference. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 158–165. Springer, Heidelberg (2011)
Schmidt, S., Kappes, J., Bergtholdt, M., Pekar, V., Dries, S., 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)
Huang, S.H., Chu, Y.H., Lai, S.H., Novak, C.L.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. TMI 28(10), 1595–1605 (2009)
Kelm, B.M., Zhou, S.K., Suehling, M., Zheng, Y., Wels, M., Comaniciu, D.: Detection of 3D spinal geometry using iterated marginal space learning. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 96–105. Springer, Heidelberg (2011)
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)
Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. MedIA 13(3), 471–482 (2009)
Zhan, Y., Maneesh, D., Harder, M., Zhou, X.S.: Robust MR Spine Detection Using Hierarchical Learning and Local Articulated Model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 141–148. Springer, Heidelberg (2012)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: ICML, pp. 96–103 (2008)
Budvytis, I., Badrinarayanan, V., Cipolla, R.: Semi-supervised video segmentation using tree structured graphical models. In: CVPR, pp. 2257–2264 (2011)
Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in CT volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis (2009)
Cheng, Y.: Mean shift, mode seeking, and clustering. PAMI 17(8), 790–799 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A. (2013). Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40763-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40762-8
Online ISBN: 978-3-642-40763-5
eBook Packages: Computer ScienceComputer Science (R0)