Abstract
Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.
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Loubele, M., Maes, F., Schutyser, F., Marchal, G., et al.: Assessment of bone segmentation quality of cone-beam CT versus multislice spiral CT: a pilot study. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 102, 225–234 (2006)
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., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 968–975. Springer, Heidelberg (2009)
Hassan, B.A.: Applications of Cone Beam Computed Tomography in Orthodontics and Endodontics (2010)
Kainmueller, D., Lamecker, H., Seim, H., Zinser, M., Zachow, S.: Automatic Extraction of Man-dibular Nerve and Bone from Cone-Beam CT Data. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, 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., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 451–458. Springer, Heidelberg (2011)
Suebnukarn, S., Haddawy, P., Dailey, M., Cao, D.: Interactive Segmentation and Three-Dimension Reconstruction for Cone-Beam Computed-Tomography Images. NECTEC Technical Journal 8, 154–161 (2008)
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., et al.: Multi-atlas based seg-mentation of brain images: Atlas selection and its effect on accuracy. NeuroImage 46, 726–738 (2009)
Rousseau, F., Habas, P.A., Studholme, C.: A Supervised Patch-Based Approach for Human Brain Labeling. TMI 30, 1852–1862 (2011)
Coupé, P., Manjón, J., Fonov, V., Pruessner, J., et al.: Patch-based segmentation using ex-pert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54, 940–954 (2011)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., et al.: elastix: A Toolbox for Intensity-Based Medical Image Registration. TMI 29, 196–205 (2010)
Wang, J., Yang, J., Yu, K., Lv, F., et al.: Locality-constrained Linear Coding for image classification. In: CVPR, pp. 3360–3367 (2010)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., et al.: Robust Face Recognition via Sparse Representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
Zou, H., Hastie, T.: Regularization and variable selection via the Elastic Net. Journal of the Royal Statistical Society, Series B 67, 301–320 (2005)
Li, C.M., Kao, C.Y., Gore, J.C., Ding, Z.H.: Minimization of Region-Scalable Fitting Energy for Image Segmentation. TIP 17, 1940–1949 (2008)
Goldstein, T., Bresson, X., Osher, S.: Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction. CAM Report, UCLA (2009)
Brox, T., Cremers, D.: On Local Region Models and a Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional. IJCV 84, 184–193 (2009)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. IJCV 22, 61–79 (1997)
Liao, S., Gao, Y., Shen, D.: Sparse patch based prostate segmentation in CT images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 385–392. Springer, Heidelberg (2012)
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Wang, L. et al. (2013). Automated Segmentation of CBCT Image Using Spiral CT Atlases and Convex Optimization. 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 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_32
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DOI: https://doi.org/10.1007/978-3-642-40760-4_32
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