Abstract
Modeling and analysis of MR images of the early developing human brain is a challenge because of the transient nature of different tissue classes during brain growth. To address this issue, a statistical model that can capture the spatial variation of structures over time is needed. Here, we present an approach to building a spatio-temporal model of tissue distribution in the developing brain which can incorporate both developed tissues as well as transient tissue classes such as the germinal matrix by using constrained higher order polynomial models. This spatio-temporal model is created from a set of manual segmentations through groupwise registration and voxelwise non-linear modeling of tissue class membership, that allows us to represent the appearance as well as disappearance of the transient brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific tissue probability maps and use them to initialize an EM segmentation of the fetal brain tissues. The approach is evaluated using clinical MR images of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Results indicate improvement in performance of atlas-based EM segmentation provided by higher order temporal models that capture the variation of tissue occurrence over time.
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Prastawa, M., Gilmore, J.H., Lin, W., Gerig, G.: Automatic segmentation of MR images of the developing newborn brain. Med. Image Anal. 9(5), 457–466 (2005)
Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., Edwards, A.D., Rueckert, D., Hajnal, J.V.: Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 38(3), 461–477 (2007)
Murgasova, M., Dyet, L., Edwards, D., Rutherford, M., Hajnal, J., Rueckert, D.: Segmentation of brain MRI in young children. Acad. Radiol. 14(11), 1350–1366 (2007)
Yoon, U., Fonov, V.S., Perusse, D., Evans, A.C.: The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 45(3), 769–777 (2009)
Studholme, C., Cardenas, V.A., Weiner, M.W.: Multiscale image and multiscale deformation of brain anatomy for building average brain atlases. In: Medical Imaging: Image Processing. In: Proc. SPIE, vol. 4322 (2001)
Prayer, D., Kasprian, G., Krampl, E., Ulm, B., Witzani, L., Prayer, L., Brugger, P.C.: MRI of normal fetal brain development. Eur. J. Radiol. 57(2), 199–216 (2006)
Kim, K., Hansen, M.F., Habas, P.A., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Intersection-based registration of slice stacks to form 3D images of the human fetal brain. In: Proc. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1167–1170 (2008)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit. 32(1), 71–86 (1999)
Pohl, K.M., Fisher, J., Shenton, M., McCarley, R.W., Grimson, W.E.L., Kikinis, R., Wells, W.M.: Logarithm odds maps for shape representation. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 955–963. Springer, Heidelberg (2006)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 885–896 (1999)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 897–908 (1999)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
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Habas, P.A., Kim, K., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C. (2009). A Spatio-temporal Atlas of the Human Fetal Brain with Application to Tissue Segmentation. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_36
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DOI: https://doi.org/10.1007/978-3-642-04268-3_36
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