Abstract
In this paper, we describe a fully automatic CAD system for spine detection in CT data followed by vertebra identification and segmentation. There are several basic problems: spine detection including the determination of spinal axis in spinal CT data, a localisation of individual vertebrae and identification of their types (order in spine) in case of incomplete scans of spine and also the final vertebra segmentation. By a subjective strict expert validation, the algorithm provides 82.6% of fully correct vertebra segmentations. Based on that, it seems to be routinely usable and fully applicable in preparation for the following automatic spine bone lesion analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Datasets of the spinal CT data on the SpineWeb website is available from: http://spineweb.digitalimaginggroup.ca
- 2.
Mean Absolute surface distance
References
Hanaoka, S., Nomura, Y., Nemoto, M., Masutani, Y., Maeda, E., Yoshikawa, T., Hayashi, N., Yoshioka, N., Ohtomo, K.: Automated segmentation method for spinal column based on a dual elliptic column model and its application for virtual spinal straightening. Journal of computer assisted tomography 34(1), 156–162 (2010)
Archip, N., Erard, P.J., Egmont-Petersen, M., Haefliger, J.M., Germond, J.F.: A knowledge-based approach to automatic detection of the spinal cord in ct images. IEEE Transactions on Medical Imaging 21(12), 1504–1516 (2002)
Burnett, S.S., Starkschall, G., Stevens, C.W., Liao, Z.: A deformable-model approach to semi-automatic segmentation of ct images demonstrated by application to the spinal canal. Medical physics 31(2), 251–263 (2004)
Forsberg, D.: Atlas-based registration for accurate segmentation of thoracic and lumbar vertebrae in ct data. In: Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, pp. 49–59. Springer (2015)
Stern, D., Vrtovec, T., Pernus, F., Likar, B.: Automated determination of the centers of vertebral bodies and intervertebral discs in ct and mr lumbar spine images. In: Medical Imaging: Image Processing. p. 762350 (2010)
Glocker, B., Feulner, J., Criminisi, A., Haynor, D., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view ct scans. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012. pp. 590–598. Springer (2012)
Cai, Y., Osman, S., Sharma, M., Landis, M., Li, S.: Multi-modality vertebra recognition in arbitrary views using 3d deformable hierarchical model. IEEE transactions on medical imaging 34(8), 1676–1693 (2015)
Hardisty, M., Gordon, L., Agarwal, P., Skrinskas, T., Whyne, C.: Quantitative characterization of metastatic disease in the spine. part i. semiautomated segmentation using atlas-based deformable registration and the level set method. Medical physics 34(8), 3127–3134 (2007)
Peter, R., Malinsky, M., Ourednicek, P., Jan, J.: 3d ct spine data segmentation and analysis of vertebrae bone lesions. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. pp. 2376–2379. IEEE (2013)
Mastmeyer, A., Engelke, K., Fuchs, C., Kalender, W.A.: A hierarchical 3d segmentation method and the definition of vertebral body coordinate systems for qct of the lumbar spine. Medical image analysis 10(4), 560–577 (2006)
Courbot, J.B., Rust, E., Monfrini, E., Collet, C.: 2-step robust vertebra segmentation. In: Image Processing Theory, Tools and Applications (IPTA), 2015 International Conference on. pp. 157–162. IEEE (2015)
Dai, Y., Niebur, G.L.: A semi-automated method for hexahedral mesh construction of human vertebrae from ct scans. Computer methods in biomechanics and biomedical engineering 12(5), 599–606 (2009)
Ibragimov, B., Korez, R., Likar, B., Pernuš, F., Vrtovec, T.: Interpolation-based detection of lumbar vertebrae in ct spine images. In: Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, pp. 73–84. Springer (2015)
Chu, C., Belavý, D.L., Armbrecht, G., Bansmann, M., Felsenberg, D., Zheng, G.: Fully automatic localization and segmentation of 3d vertebral bodies from ct/mr images via a learning-based method. PloS one 10(11), e0143327 (2015)
Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in ct images. Medical image analysis 13(3), 471–482 (2009)
Kadoury, S., Labelle, H., Paragios, N.: Spine segmentation in medical images using manifold embeddings and higher-order mrfs. IEEE transactions on medical imaging 32(7), 1227–1238 (2013)
Hammernik, K., Ebner, T., Stern, D., Urschler, M., Pock, T.: Vertebrae segmentation in 3d ct images based on a variational framework. In: Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, pp. 227–233. Springer (2015)
Cheng, E., Liu, Y., Wibowo, H., Rai, L.: Learning-based spine vertebra localization and segmentation in 3d ct image. In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. pp. 160–163. IEEE (2016)
Chen, H., Shen, C., Qin, J., Ni, D., Shi, L., Cheng, J.C., Heng, P.A.: Automatic localization and identification of vertebrae in spine ct via a joint learning model with deep neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 515–522. Springer (2015)
Huang, J., Jian, F., Wu, H., Li, H.: An improved level set method for vertebra ct image segmentation. Biomedical engineering online 12(1), 48 (2013)
Štern, D., Likar, B., Pernuš, F., Vrtovec, T.: Parametric modelling and segmentation of vertebral bodies in 3d ct and mr spine images. Physics in medicine and biology 56(23), 7505 (2011)
Jakubicek, R., Chmelik, J., Jan, J.: Vertebrae segmentation in 3d ct data: A review of methods and evaluation approaches. Current Medical Imaging Reviews 13(4), 1–14 (2017), iSSN 1573-4056/1875-6603. [in press]
Yao, J., Burns, J.E., Forsberg, D., Seitel, A., Rasoulian, A., Abolmaesumi, P., Hammernik, K., Urschler, M., Ibragimov, B., Korez, R., et al.: A multi-center milestone study of clinical vertebral ct segmentation. Computerized Medical Imaging and Graphics 49, 16–28 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
These results were obtained through international cooperation between the Brno University of Technology and Philips Healthcare Netherlands. No conflicts of interest were identified.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jakubicek, R., Chmelik, J., Jan, J., Ourednicek, P., Lambert, L., Gavelli, G. (2019). Fully Automatic CAD System for Spine Localisation and Vertebra Segmentation in CT Data. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/1. Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_40
Download citation
DOI: https://doi.org/10.1007/978-981-10-9035-6_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9034-9
Online ISBN: 978-981-10-9035-6
eBook Packages: EngineeringEngineering (R0)