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Robust Cortical Thickness Measurement with LOGISMOS-B

  • Ipek Oguz
  • Milan Sonka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Cortical thickness (CT) is an important morphometric measure that has implications for psychiatric and neurologic processes. We propose a novel approach for automatically computing CT in an accurate and robust manner using LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain. LOGISMOS-B is a cortical surface segmentation method based on LOGISMOS graph segmentation and generalized gradient vector flows. We evaluate our method on two different datasets (n = 83 total). The results show that LOGISMOS-B is more accurate than the popular FreeSurfer (FS) method and provides more reliable thickness measurements across a variety of challenging images. LOGISMOS-B accurately recovers known CT patterns, both across cortical lobes and locally, such as between the banks of the central sulcus, in healthy subjects and MS patients. Manual landmarks indicate a signed surface distance of 0.081±0.447mm for WM and 0.018±0.498mm for LOGISMOS-B, compared to 0.263±0.452mm for WM and − 0.167±0.556mm for GM for FS, highlighting the surface placement accuracy of LOGISMOS-B. Finally, a regresion study shows that LOGISMOS-B provides strong correlation with age and plausible annual thinning rates across the cortex, with locally discerning thinning patterns, in agreement with the literature.

Keywords

LOGISMOS cortical thickness optimal multi-surface segmentation cortical reconstruction generalized gradient vector flow 

References

  1. 1.
    Clarkson, M., Cardoso, J., Ridgway, G., Leung, K., Rohrer, J., Fox, N., Ourselin, S.: A comparison of voxel and surface based CT estimation methods. NeuroImage 57, 856–865 (2011)CrossRefGoogle Scholar
  2. 2.
    von Economo, C.: The Cytoarchitectonics of the Human Cerebral Cortex. Oxford Univ. Press, London (1929)Google Scholar
  3. 3.
    Fischl, B., Dale, A.: Measuring the thickness of the human cortex from MRI. PNAS (2000)Google Scholar
  4. 4.
    Han, X., Pham, D., Tosun, D., Rettmann, M., Xu, C., Prince, J.: CRUISE. NeuroImage (2004)Google Scholar
  5. 5.
    Hutton, C., Draganski, B., Ashburner, J., Weiskopf, N.: A comparison between VBCT and VBM in normal aging. Neuroimage 48, 371–380 (2009)CrossRefGoogle Scholar
  6. 6.
    Jones, S., Buchbinder, B., Aharon, I.: 3D mapping of CT using Laplace’s equation. HBM 11(1), 12–32 (2000)CrossRefGoogle Scholar
  7. 7.
    Kim, E.Y., Johnson, H.J.: Robust multi-site MR data processing: Iterative optimization of bias correction, tissue classification, and registration. Front Neuroinform 7(29), 1–18 (2013)Google Scholar
  8. 8.
    Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., Evans, A.C.: Automated 3D extraction and eval. of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage 27, 210–221 (2005)CrossRefGoogle Scholar
  9. 9.
    Luders, E., Narr, K., Thompson, P., Rex, D., Jancke, L., Toga, A.: Hemispheric asymmetries in CT. Cereb Cortex 16, 1232–1238 (2006)CrossRefGoogle Scholar
  10. 10.
    Meyer, J., Roychowdhury, S., Russell, E., Callahan, C., Gitelman, D., Mesulam, M.: Location of the CS via CT of the precentral and postcentral gyri on MR. Am. J. Neuroradiol. 17 (1996)Google Scholar
  11. 11.
    Oguz, I., Sonka, M.: LOGISMOS-B: Layered optimal graph image segmentation of multiple objects and surfaces for the brain. IEEE Trans. Med. Imaging 33, 1–16 (2014)CrossRefGoogle Scholar
  12. 12.
    Park, D.C., Reuter-Lorenz, P.: The adaptive brain: Aging and neurocognitive scaffolding. Annu. Rev. Psychol. 60(1), 173–196 (2009)CrossRefGoogle Scholar
  13. 13.
    Pichon, E., Nain, D., Niethammer, M.: A Laplace equation approach for shape comparison. SPIE Medical Imaging (2006)Google Scholar
  14. 14.
    Sailer, M., Fischl, B., Salat, D., Tempelmann, C., Schönfeld, M., Busa, E., Bodammer, N., Heinze, H., Dale, A.: Focal thinning of the cerebral cortex in MS. Brain 126, 1734–1744 (2003)CrossRefGoogle Scholar
  15. 15.
    Shiee, N., Bazin, P., Cuzzocreo, J., Ye, C., Kishore, B., Carass, A., Calabresi, P., Reich, D., Prince, J., Pham, D.: Robust reconstruction of the human brain cortex in the presence of the WM lesions: Method and validation. HBM (2013)Google Scholar
  16. 16.
    Tustison, N.J., Avants, B.B., Cook, P.A., Song, G., Das, S., Strien, N.V., Stone, J.R., Gee, J.C.: The ANTS CT processing pipeline. SPIE Medical Imaging (2013)Google Scholar
  17. 17.
    Vachet, C., Hazlett, H.C., Niethammer, M., Oguz, I., Cates, J., Whitaker, R., Piven, J., Styner, M.: Group-wise automatic mesh-based analysis of CT. SPIE Medical Imaging (2011)Google Scholar
  18. 18.
    Xu, C., Prince, J.: GGVF external forces for active contours. Sig. Proc. 71, 131–139 (1998)CrossRefzbMATHGoogle Scholar
  19. 19.
    Yin, Y., Zhang, X., Williams, R., Wu, X., Anderson, D.D., Sonka, M.: LOGISMOS: cartilage segmentation in the knee joint. IEEE TMI 29, 2023–2037 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ipek Oguz
    • 1
  • Milan Sonka
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of IowaIowa CityUSA

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