A statistical shape model of the human second cervical vertebra
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Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population.
The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2.
The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling.
The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.
KeywordsStatistical shape model Second cervical vertebra Non-rigid image registration Segmentation Principal component analysis
The Swiss National Science Foundation (SNSF) supported this study. The authors thank KB Medical for their help and support with the project.
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Ishikawa Y, Kanemura T, Yoshida G, Ito Z, Muramoto A, Ohno S (2010) Clinical accuracy of three-dimensional fluoroscopy-based computer-assisted cervical pedicle screw placement: a retrospective comparative study of conventional versus computer-assisted cervical pedicle screw placement. J Neurosurg Spine 13(5):606–611PubMedCrossRefGoogle Scholar
- 8.Weidner A, Wähler M, Chiu ST, Ullrich CG (2000) Modification of c1–c2 transarticular screw fixation by image-guided surgery. Spine 25(20):2668–2673, discussion 2674Google Scholar
- 19.Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341PubMedCentralPubMedCrossRefGoogle Scholar
- 20.3D slicer. www.slicer.org/
- 22.Aslan M, Farag A, Arnold B, Xiang P (2011) Segmentation of vertebrae using level sets with expectation maximization algorithm. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro, pp 2010–2013Google Scholar
- 23.Aslan M, Ali A, Farag A, Rara H, Arnold B, Xiang P (2010) 3d vertebral body segmentation using shape based graph cuts. In: 2010 20th international conference on pattern recognition (ICPR), pp 3951–3954Google Scholar
- 29.Mirzaalian H, Wels M, Heimann T, Kelm B, Suehling M (2013) Fast and robust 3d vertebra segmentation using statistical shape models. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 3379–3382Google Scholar
- 31.Heitz G, Rohlfing T, Maurer CR Jr (2005) Statistical shape model generation using nonrigid deformation of a template mesh, pp 1411–1421Google Scholar
- 32.Rasoulian A, Rohling R, Abolmaesumi P (2012) Group-wise registration of point sets for statistical shape models. IEEE Trans Med Imaging 31(11):2025–2034Google Scholar
- 34.Lüthi M, Jud C, Vetter T (2011) Using landmarks as a deformation prior for hybrid image registration. In: Proceedings of the 33rd international conference on pattern recognition, Berlin, Heidelberg, DAGM’11, pp 196–205Google Scholar
- 36.Lüthi M, Jud C, Vetter T (2013) A unified approach to shape model fitting and non-rigid registration. In: Machine learning in medical imaging, no. 8184 in lecture notes in computer science, pp 66–73Google Scholar
- 37.Styner MA, Rajamani KT, Nolte LP, Zsemlye G, Székely G, Taylor CJ, Davies RH (2003) Evaluation of 3d correspondence methods for model building. In: Taylor C, Noble JA (eds) Information processing in medical imaging, no. 2732 in lecture notes in computer science, pp 63–75Google Scholar
- 38.Kistler M, Bonaretti S, Pfahrer M, Niklaus R, Buchler P (2013) The virtual skeleton database: An open access repository for biomedical research and collaboration. J Med Internet Res 15(11):e245Google Scholar
- 40.Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge, MAGoogle Scholar
- 42.The insight segmentation and registration toolkit. www.itk.org
- 43.Lüthi M, Albrecht T, Gass T, Goksel O, Kistler M, Bousleiman H, Reyes M, Buechler P, Cattin PC, Vetter T (2012) Statismo—a framework for PCA based statistical models. Insight J 1:1–18Google Scholar