Abstract
We present an iterative bootstrapping framework to create and analyze statistical atlases of bony anatomy such as the human pelvis from a large collection of CT data sets. We create an initial tetrahedral mesh representation of the target anatomy and use deformable intensity-based registration to create an initial atlas. This atlas is used as prior information to assist in deformable registration/segmentation of our subject image data sets, and the process is iterated several times to remove any bias from the initial choice of template subject and to improve the stability and consistency of mean shape and variational modes. We also present a framework to validate the statistical models. Using this method, we have created a statistical atlas of full pelvis anatomy with 110 healthy patient CT scans. Our analysis shows that any given pelvis shape can be approximated up to an average accuracy of 1.5036 mm using the first 15 principal modes of variation. Although a particular intensity-based deformable registration algorithm was used to produce these results, we believe that the basic method may be adapted readily for use with any registration method with broadly similar characteristics.
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Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision Image Understanding 61(1), 38–59 (1995)
Yao, J., Taylor, R.H.: Non-rigid registration and correspondence finding in medical image analysis using multiple-layer flexible mesh template matching. IJPRAI 17(7), 1145–1165 (2003)
Querol, L., Buchler, P., Rueckert, D., Nolte, L.P., Ballester, M.: Statistical finite element model for bone shape and biomechanical properties. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 405–411. Springer, Heidelberg (2006)
Wu, C., Murtha, P.E., Mor, A.B., Jaramaz, B.: A two-level method for building a statistical shape atlas. In: CAOS (2005)
Sadowsky, O., Ramamurthi, K., Ellingsen, L., Chintalapani, G., Prince, J., Taylor, R.: Atlas-assisted tomography: registration of a deformable atlas to compensate for limited-angle cone-beam trajectory. In: IEEE International Symposium for Biomedical Imaging pp. 1244–1247 (2006)
Cutting, C.B., Bookstein, F.L., Haddad, B., Dean, D., Kim, D.: Spline-based approach for averaging three-dimensional curves and surfaces. Mathematical methods in medical imaging II, SPIE 2035, 29–44 (1993)
Chui, H., Zhang, J., Rangarajan, A.: Unsupervised learning of an atlas from unlabeled point-sets. IEEE Trans. Pattern Analysis and Machine Intelligence 26(2), 160–173 (2004)
Rueckert, D., Frangi, A., Schnabel, J.: Automatic construction of 3d statistical deformation models using non-rigid registration. In: Medical Image Computing and Computer Assisted Intervention, pp. 77–84 (2001)
Ellingsen, L., Prince, J.: Deformable registration of ct pelvis images using mjolnir. In: IEEE 7th Nordic Signal Processing Symposium (NORSIG) (2006)
Cootes, T., Beeston, C., Edwards, G., Taylor, C.: A unified framework for atlas matching using active appearance models. In: IPMI, pp. 322–333 (1999)
Shen, D., Davatzikos, C.: Adaptive-focus statistical shape model for segmentation of 3d mr structures. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 206–215. Springer, Heidelberg (2000)
Mohamed, A., Davatzikos, C.: An approach to 3d finite element mesh generation from segmented medical images. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2004)
Besl, P., McKay, N.: A method for registration of 3-d shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
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Chintalapani, G., Ellingsen, L.M., Sadowsky, O., Prince, J.L., Taylor, R.H. (2007). Statistical Atlases of Bone Anatomy: Construction, Iterative Improvement and Validation. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75757-3_61
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DOI: https://doi.org/10.1007/978-3-540-75757-3_61
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