Abstract
A major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulty for accurate segmentation of bony structures from soft tissues, as well as separation of mandible from maxilla. In this paper, we present a novel fully automated method for CBCT image segmentation. Specifically, we first employ majority voting to estimate the initial probability maps of mandible and maxilla. We then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of classifier. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of classifier. By iteratively training the subsequent classifier and the updated segmentation probability maps, we can derive a sequence of classifiers. Experimental results on 30 CBCTs show that the proposed method achieves the state-of-the-art performance.
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
References
Wang, L., Ren, Y., Gao, Y., Tang, Z., Chen, K.C., Li, J., Shen, S.G., Yan, J., Lee, P.K., Chow, B., Xia, J.J., Shen, D.: Estimating patient-specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation. Med. Phys. 42, 5809 (2015)
Wang, L., Chen, K.C., Gao, Y., Shi, F., Liao, S., Li, G., Shen, S.G.F., Yan, J., Lee, P.K.M., Chow, B., Liu, N.X., Xia, J.J., Shen, D.: Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Med. Phys. 41, 043503 (2014)
Le, B.H., Deng, Z., Xia, J., Chang, Y.-B., Zhou, X.: An interactive geometric technique for upper and lower teeth segmentation. In: Yang, G.-Z., et al. (eds.) MICCAI 2009, vol. 5762, pp. 968–975. Springer, Berlin Heidelberg (2009)
Hassan, B.A.: Applications of Cone Beam Computed Tomography in Orthodontics and Endodontics. Thesis, Reading University, VU University Amsterdam (2010)
He, L., Zheng, S.F., Wang, L.: Integrating local distribution information with level set for boundary extraction. J. Vis. Commun. Image Represent. 21, 343–354 (2010)
Kainmueller, D., Lamecker, H., Seim, H., Zinser, M., Zachow, S.: Automatic extraction of mandibular nerve and bone from cone-beam CT data. In: Yang, G.-Z., et al. (eds.) MICCAI 2009, vol. 5762, pp. 76–83. Springer, Heidelberg (2009)
Gollmer, S.T., Buzug, T.M.: Fully automatic shape constrained mandible segmentation from cone-beam CT data. In: ISBI, pp. 1272–1275 (2012)
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Deformable segmentation via sparse shape representation. In: Fichtinger, G., et al. (eds.) MICCAI 2011, vol. 6892, pp. 451–458. Springer, Heidelberg (2011)
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16, 265–277 (2012)
Zhang, S.T., Zhan, Y.Q., Metaxas, D.N.: Deformable segmentation via sparse representation and dictionary learning. Med. Image Anal. 16, 1385–1396 (2012)
Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D.: Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 89, 152–164 (2014)
Wang, L., Shi, F., Li, G., Gao, Y., Lin, W., Gilmore, J.H., Shen, D.: Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 84, 141–158 (2014)
Shi, F., Wang, L., Wu, G.R., Li, G., Gilmore, J.H., Lin, W.L., Shen, D.: Neonatal atlas construction using sparse representation. Hum. Brain Mapp. 35, 4663–4677 (2014)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Zikic, D., Glocker, B., Criminisi, A.: Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med. Image Anal. 18, 1262–1273 (2014)
Wang, L., Gao, Y., Shi, F., Li, G., Gilmore, J.H., Lin, W., Shen, D.: LINKS: learning-based multi-source integration framework for segmentation of infant brain images. NeuroImage 108, 160–172 (2015)
Zikic, D., Glocker, B., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., et al. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, vol. 7512, pp. 369–376. Springer, Heidelberg (2012)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744–1757 (2010)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)
Sutton, C., McCallum, A., Rohanimanesh, K.: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. J. Mach. Learn. Res. 8, 693–723 (2007)
Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11, 520–527 (2007)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509–522 (2002)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13, 415–425 (2002)
Zikic, D., Glocker, B., Criminisi, A.: Atlas encoding by randomized forests for efficient label propagation. In: Mori, K., et al. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, vol. 8151, pp. 66–73. Springer, Berlin Heidelberg (2013)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, L. et al. (2016). Automated Segmentation of CBCT Image with Prior-Guided Sequential Random Forest. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2015. Lecture Notes in Computer Science(), vol 9601. Springer, Cham. https://doi.org/10.1007/978-3-319-42016-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-42016-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42015-8
Online ISBN: 978-3-319-42016-5
eBook Packages: Computer ScienceComputer Science (R0)