Abstract
Babies born prematurely are at increased risk of adverse neurodevelopmental outcomes. Recent advances suggest that measurement of brain volumes can help in defining biomarkers for neurodevelopmental outcome. These techniques rely on an accurate segmentation of the MRI data. However, due to lack of contrast, partial volume (PV) effect, the existence of both hypo- and hyper-intensities and significant natural and pathological anatomical variability, the segmentation of neonatal brain MRI is challenging. We propose a pipeline for image segmentation that uses a novel multi-model Maximum a posteriori Expectation Maximisation (MAP-EM) segmentation algorithm with a prior over both intensities and the tissue proportions, a B0 inhomogeneity correction, and a spatial homogeneity term through the use of a Markov Random Field. This robust and adaptive technique enables the segmentation of images with high anatomical disparity from a normal population. Furthermore, the proposed method implicitly models Partial Volume, mitigating the problem of neonatal white/grey matter intensity inversion. Experiments performed on a clinical cohort show expected statistically significant correlations with gestational age at birth and birthweight. Furthermore, the proposed method obtains statistically significant improvements in Dice scores when compared to the a Maximum Likelihood EM algorithm.
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Keywords
- Markov Random Field
- Maximum Likelihood Expectation Maximisation
- Dice Score
- Adverse Neurodevelopmental Outcome
- Tissue Proportion
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Marlow, N., Wolke, D., Bracewell, M.A., Samara, M.: Neurologic and developmental disability at six years of age after extremely preterm birth. New England Journal of Medicine 352(1), 9–19 (2005)
Boardman, J., Craven, C., Valappil, S., Counsell, S., Dyet, L., Rueckert, D., Aljabar, P., Rutherford, M., Chew, A., Allsop, J., Cowan, F., Edwards, A.: A common neonatal image phenotype predicts adverse neurodevelopmental outcome in children born preterm. NeuroImage 52(2), 409–414 (2010)
Weisenfeld, N.I., Warfield, S.K.: Automatic segmentation of newborn brain mri. NeuroImage 47(2), 564–572 (2009)
Song, Z., Awate, S., Licht, D., Gee, J.: Clinical neonatal brain mri segmentation using adaptive nonparametric data models. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 883–890. Springer, Heidelberg (2007)
Yu, X., Zhang, Y., Lasky, R.E., Parikh, N.A., Narayana, P.A.: Comprehensive brain mri segmentation in high risk preterm newborns. PLoS ONE 5(11) (2010)
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)
Wells, W., Grimson, W.E., Kikinis, R., Jolesz, F.A.: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15(4), 429–442 (1996)
Zhang, Y., Brady, M., Smith, S.M.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE TMI 18(10) (1999)
Zhang, J.: The mean field theory in em procedures for markov random fields. IEEE Transactions on Signal Processing 40(10), 2570–2583 (1992)
Shiee, N., Bazin, P.L., Cuzzocreo, J.L., Blitz, A., Pham, D.L.: Segmentation of brain images using adaptive atlases with application to ventriculomegaly
Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block matching: A general framework to improve robustness of rigid registration of medical images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 557–566. Springer, Heidelberg (2000)
Modat, M., Ridgway, G., Taylor, Z., Lehmann, M., Barnes, J., Hawkes, D., Fox, N., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine (October 2009)
Kuklisova-Murgasova, M., Aljabar, P., Srinivasan, L., Counsell, S.J., Gousias, I.S., Boardman, J.P., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: A dynamic 4d probabilistic atlas of the developing brain. NeuroImage 54(4) (2011)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: A unifying framework for partial volume segmentation of brain MR images. IEEE Transactions on Medical Imaging 22(1), 105–119 (2003)
Ruan, S., Jaggi, C., Fadili, J., Bloyet, D.: Brain tissue classification of magnetic resonance images using partial volume modeling. IEEE TMI 19(12) (December 2000)
Cardoso, M.J., Clarkson, M.J., Ridgway, G.R., Modat, M., Fox, N.C., Ourselin, S., The Alzheimer’s Disease Neuroimaging Initiative.: LoAd: A locally adaptive cortical segmentation algorithm. NeuroImage 56(3), 1386–1397 (2011)
Kitamoto, A., Takagi, M.: Image classification using probabilistic models that reflect the internal structure of mixels. Pattern Analysis and Applications 2 (1999)
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Cardoso, M.J. et al. (2011). Adaptive Neonate Brain Segmentation. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_47
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DOI: https://doi.org/10.1007/978-3-642-23626-6_47
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