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Automatic Extraction of the Midsagittal Surface from Brain MR Images using the Kullback–Leibler Measure

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Abstract

The midsagittal surface separates the two hemispheres of the cerebrum. This surface is often typified as a geometrical plane: the midsagittal plane. However, in subjects with a considerable amount of naturally occurring brain torque, the midsagittal surface deviates to a large extent from a plane. In the present study, an automated method to extract the midsagittal surface is proposed, evaluated on a large dataset, and compared to a conventional midsagittal plane representation. The midsagittal plane was extracted from MR images with a technique based on the Kullback–Leibler measure. This plane was used to initialize a surface, that was deformed to represent the midsagittal surface. One hundred subjects were selected from the SMART-MR study: fifty subjects with brain torque and fifty random subjects. Manual delineations of the midsagittal surface were used for evaluation. The extracted midsagittal planes and surfaces were compared to the manual delineations by assessing the absolute volume of misclassified cerebrum tissue. The midsagittal surface resulted in significantly better separations of the hemispheres. In the randomly selected subjects, the error reduced from 2.71 ± 1.05 ml to 2.20 ± 0.66 ml and in subjects with brain torque from 4.85±2.79 ml to 2.23±0.77 ml, with improvements up to 16.6 ml in individual subjects with marked brain torque.

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References

  • Anbeek, P., Vincken, K.L., van Bochove, G.S., van Osch, M.J.P., van der Grond, J. (2005). Probabilistic segmentation of brain tissue in mr imaging. NeuroImage, 27(4), 795–804. doi:10.1016/j.neuroimage.2005.05.046.

    Article  PubMed  Google Scholar 

  • Ashburner, J., & Friston, K.J. (2000). Voxel-based morphometry - the methods. NeuroImage, 11(6 I), 805–821. doi:10.1006/nimg.2000.0582, cited By (since 1996): 2013.

    Article  CAS  PubMed  Google Scholar 

  • Balzeau, A., Gilissen, E., Grimaud-Hervé, D. (2012). Shared pattern of endocranial shape asymmetries among great apes, anatomically modern humans, and fossil hominins. PLoS ONE, 7(1), e29,581. doi:10.1371/journal.pone.0029581.

    Article  CAS  Google Scholar 

  • Bochkanov, S., & Bystritsky, V. (2012). Alglib www.alglib.net.

  • Brummer, M.E. (1991). Hough transform detection of the longitudinal fissure in tomographic head images. IEEE Transactions on Medical Imaging, 10(1), 74–81. doi:10.1109/42.75613.

    Article  CAS  PubMed  Google Scholar 

  • Fonov, V.S., Evans, A.C., McKinstry, R.C., Almli, C.R., Collins, D.L. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47(Supplement 1), S102. doi:10.1016/S1053-8119(09)70884-5, organization for Human Brain Mapping 2009 Annual Meeting.

    Article  Google Scholar 

  • Fonov, V.S., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313–327. doi:10.1016/j.neuroimage.2010.07.033.

    Article  PubMed Central  PubMed  Google Scholar 

  • Galaburda, A.M., LeMay, M., Kemper, T.L., Geschwind, N. (1978). Right-left asymmetrics in the brain. Science, 199(4331), 852–856. doi:10.1126/science.341314.

    Article  CAS  PubMed  Google Scholar 

  • Geerlings, M.I., Appelman, A.P.A., Vincken, K.L., Algra, A., Witkamp, T.D., Mali, W.P.T.M., van der Graaf, Y. (2010). Brain volumes and cerebrovascular lesions on {MRI} in patients with atherosclerotic disease. The smart-mr study. Atherosclerosis, 210(1), 130–136. doi:10.1016/j.atherosclerosis.2009.10.039.

    Article  CAS  PubMed  Google Scholar 

  • Hu, Q., & Nowinski, W.L. (2003). A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal. NeuroImage, 20(4), 2153–2165. doi:10.1016/j.neuroimage.2003.08.009.

    Article  PubMed  Google Scholar 

  • Jayasuriya, S., Liew, A., Law, N. (2013). Brain symmetry plane detection based on fractal analysis. Computerized Medical Imaging and Graphics. doi:10.1016/j.compmedimag.2013.06.001 (in press).

  • Joshi, S., Lorenzen, P., Gerig, G., Bullitt, E. (2003). Structural and radiometric asymmetry in brain images. Medical Image Analysis, 7(2), 155–170. doi:10.1016/S1361-8415(03)00002-1.

    Article  PubMed  Google Scholar 

  • Junck, L., Moen, J.G., Hutchins, G.D., Brown, M.B., Kuhl, D.E. (1990). Correlation methods for the centering, rotation, and alignment of functional brain images. Journal of Nuclear Medicine, 31(7), 1220–1226. http://jnm.snmjournals.org/content/31/7/1220.short.

    CAS  PubMed  Google Scholar 

  • King, D.E. (2012). Dlib c++ library www.dlib.net.

  • Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W. (2010). Elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging, 29(1), 196–205. doi:10.1109/TMI.2009.2035616.

    Article  PubMed  Google Scholar 

  • Kuijf, H.J., Leemans, A., Viergever, M.A., Vincken, K.L. (2013a). Assessment of methods to extract the mid-sagittal plane from brain mr images. In Proceedings SPIE (Vol. 8673, p. 86731K). doi:10.1117/12.2006858.

  • Kuijf, H.J., Viergever, M.A., Vincken, K.L. (2013b). Automatic extraction of the curved midsagittal brain surface on mr images. In Medical computer vision. Recognition techniques and applications in medical imaging, lecture notes in computer science (Vol. 7766, pp. 225–232). Springer Berlin Heidelberg. doi:10.1007/978-3-642-36620-8_22.

  • Liang, L., Rehm, K., Woods, R.P., Rottenberg, D.A. (2007). Automatic segmentation of left and right cerebral hemispheres from mri brain volumes using the graph cuts algorithm. NeuroImage, 34(3), 1160–1170. doi:10.1016/j.neuroimage.2006.07.046.

    Article  PubMed  Google Scholar 

  • Liao, C.C., Xiao, F., Wong, J.M., Chiang, I.J. (2010). Automatic recognition of midline shift on brain ct images. Computers in Biology and Medicine, 40(3), 331–339. doi:10.1016/jcompbiomed.2010.01.004.

    Article  PubMed  Google Scholar 

  • Liu, D.C., & Nocedal, J. (1989). On the limited memory bfgs method for large scale optimization. Mathematical Programming, 45(1–3), 503–528. doi:10.1007/BF01589116.

    Article  Google Scholar 

  • Liu, Y., Collins, R., Rothfus, W.E. (2001). Robust midsagittal plane extraction from normal and pathological 3d neuroradiology images. IEEE Transactions on Medical Imaging, 20(1), 175–192.

    CAS  PubMed  Google Scholar 

  • Nagel, B.J., Herting, M.M., Maxwell, E.C., Bruno, R., Fair, D. (2013). Hemispheric lateralization of verbal and spatial working memory during adolescence. Brain and Cognition, 82(1), 58–68. doi:10.1016/j.bandc.2013.02.007.

    Article  PubMed Central  PubMed  Google Scholar 

  • Nowinski, W.L., Prakash, B., Volkau, I., Ananthasubramaniam, A., Beauchamp, N.J. Jr. (2006). Rapid and automatic calculation of the midsagittal plane in magnetic resonance diffusion and perfusion images. Academic Radiology, 13(5), 652–663. doi:10.1016/j.acra.2006.01.051.

    Article  PubMed  Google Scholar 

  • Phillips, H.J. (1964). A method of determining the midsagittal plane of the cranium. American Journal of Orthodontics, 50(10), 788. doi:10.1016/0002-9416(64)90099-5.

    Article  Google Scholar 

  • Prima, S., Ourselin, S., Ayache, N. (2002). Computation of the mid-sagittal plane in 3-d brain images. IEEE Transactions on Medical Imaging, 21(2), 122–138. doi:10.1109/42.993131.

    Article  PubMed  Google Scholar 

  • Puspitasari, F., Volkau, I., Ambrosius, W., Nowinski, W. (2009). Robust calculation of the midsagittal plane in ct scans using the kullback–leibler’s measure. International Journal of Computer Assisted Radiology and Surgery, 4, 535–547. doi:10.1007/s11548-009-0366-2.

    Article  PubMed  Google Scholar 

  • Rentería, M.E. (2012). Cerebral asymmetry: a quantitative, multifactorial, and plastic brain phenotype. Twin Research and Human Genetics, 15, 401–413. doi:10.1017/thg.2012.13.

    Article  PubMed  Google Scholar 

  • Ritter, F., Boskamp, T., Homeyer, A., Laue, H., Schwier, M., Link, F., Peitgen, H.O. (2011). Medical image analysis: a visual approach. IEEE Pulse, 2(6), 60–70. doi:10.1109/MPUL.2011.942929.

    Article  PubMed  Google Scholar 

  • Sommer, I., Ramsey, N., Kahn, R., Aleman, A., Bouma, A. (2001). Handedness, language lateralisation and anatomical asymmetry in schizophrenia: meta-analysis. The British Journal of Psychiatry, 178(4), 344–351. doi:10.1192/bjp.178.4.344.

    Article  CAS  PubMed  Google Scholar 

  • Stegmann, M.B., Skoglund, K., Ryberg, C. (2005). Mid-sagittal plane and mid-sagittal surface optimization in brain mri using a local symmetry measure. In J.M. Reinhardt & J.M. Fitzpatrick (Eds.), Proceedings SPIE (Vol. 5747, pp. 568–579).

  • Toga, A.W., & Thompson, P.M. (2003). Mapping brain asymmetry. Nature Reviews Neuroscience, 4, 37–48. doi:10.1038/nrn1009.

    Article  CAS  PubMed  Google Scholar 

  • Tuzikov, A.V., Colliot, O., Bloch, I. (2003). Evaluation of the symmetry plane in 3d mr brain images. Pattern Recognition Letters, 24(14), 2219–2233. doi:10.1016/S0167-8655(03)00049-7.

    Article  Google Scholar 

  • Volkau, I., Prakash, K.B., Ananthasubramaniam, A., Aziz, A., Nowinski, W.L. (2006). Extraction of the midsagittal plane from morphological neuroimages using the kullback-leibler’s measure. Medical Image Analysis, 10(6), 863–874. doi:10.1016/j.media.2006.07.005.

    Article  PubMed  Google Scholar 

  • Vos, P.C., Biesbroek, J.M., Weaver, N.A., Velthuis, B.K., Viergever, M.A. (2013). Automatic detection and segmentation of ischemic lesions in computed tomography images of stroke patients. In Proceedings SPIE 8670, medical imaging 2013: computer-aided diagnosis (p. 867013). doi:10.1117/12.2008074.

  • Zhang, T., & Nagy, G. (2004). Surface tortuosity and its application to analyzing cracks in concrete. In Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004 (Vol. 2, pp. 851–854). doi:10.1109/ICPR.2004.1334392.

  • Zhang, Y., & Hu, Q. (2008). A pca-based approach to the representation and recognition of mr brain midsagittal plane images. In 30th annual international conference of the IEEE engineering in medicine and biology society (pp. 3916–3919). doi:10.1109/IEMBS.2008.4650066.

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Correspondence to Hugo J. Kuijf.

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Kuijf, H.J., van Veluw, S.J., Geerlings, M.I. et al. Automatic Extraction of the Midsagittal Surface from Brain MR Images using the Kullback–Leibler Measure. Neuroinform 12, 395–403 (2014). https://doi.org/10.1007/s12021-013-9215-0

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