Abstract
Traumatic brain injury (TBI) is often associated with life long neurobehavioral effects in survivors. Imaging has historically supported the detection and acute management of life-threatening complications. However, in order to predict these long term consequences in the increasing number of individuals surviving TBI, there is an emerging need for structural neuroimaging biomarkers that would facilitate detection of milder injuries, allow recovery trajectory monitoring, and identify those at risk for poor functional outcome and disability. This paper presents a methodology capable of identifying such structural biomarkers in MR images of the brain. Results are presented demonstrating the quantitative accuracy of the approach with respect to (i) highly accurate annotations from expert tracers, (ii) an alternative segmentation method in FSL, and (iii) the ability to reproduce statistically significant differences in the volumes of specific structures between well-defined clinical cohorts (TBI vs age-matched healthy controls) in a retrospective analysis study.
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References
Babalola, K., Patenaude, B., Aljabar, P., Schnabel, J., Kennedy, D., Crum, W., Smith, S., Cootes, T., Jenkinson, M., Rueckert, D.: An evaluation of four automatic methods of segmenting the subcortical structures in the brain. Neuroimage 47(4), 1435–1447 (2009)
Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)
Cootes, T., Taylor, C., Cooper, D., Graham, J., et al.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M., Vembar, M., Olszewski, M., Subramanyan, K., Lavi, G., et al.: Automatic model-based segmentation of the heart in CT images. IEEE Transactions on Medical Imaging 27(9), 1189–1201 (2008)
Fischl, B., Salat, D., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al.: Whole brain segmentation automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)
Jorge, R., Robinson, R., Moser, D., Tateno, A., Crespo-Facorro, B., Arndt, S.: Major depression following traumatic brain injury. Archives of General Psychiatry 61(1), 42 (2004)
Mathalon, D., Sullivan, E., Rawles, J., Pfefferbaum, A.: Correction for head size in brain-imaging measurements. Psychiatry Research: Neuroimaging 50(2), 121–139 (1993)
Patenaude, B., Smith, S., Kennedy, D., Jenkinson, M.: Bayesian shape and appearance models. Tech. rep., TR07BP1, FMRIB Center, University of Oxford (2007)
Peters, J., Ecabert, O., Meyer, C., Kneser, R., Weese, J.: Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Medical Image Analysis 14(1), 70 (2010)
Weese, J., Kaus, M.R., Lorenz, C., Lobregt, S., Truyen, R., Pekar, V.: Shape constrained deformable models for 3D medical image segmentation. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 380–387. Springer, Heidelberg (2001)
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Zagorchev, L., Meyer, C., Stehle, T., Kneser, R., Young, S., Weese, J. (2011). Evaluation of Traumatic Brain Injury Patients Using a Shape-Constrained Deformable Model. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds) Multimodal Brain Image Analysis. MBIA 2011. Lecture Notes in Computer Science, vol 7012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24446-9_15
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DOI: https://doi.org/10.1007/978-3-642-24446-9_15
Publisher Name: Springer, Berlin, Heidelberg
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