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Large-scale shape analysis of human brain MRI data

Arno Klein1*, Joachim Giard2, Forrest S. Bao1, Martin Reuter3, Eliezer Stavsky4, Yrjo Hame4, B N. Nichols5, Satrajit S. Ghosh6 and Jason Tourville7
  • 1 Stony Brook University, Psychiatry & Behavioral Science, United States
  • 2 ICTEAM, Catholique de Louvain, Belgium, Belgium
  • 3 Harvard Medical School, United States
  • 4 Columbia University, United States
  • 5 University of Washington, United States
  • 6 MIT, United States
  • 7 Boston University, United States

Human brain shape databases are useful for morphometric studies of healthy and patient populations. They provide scientists with shape measures for comparison with their own MRI data, as well as to train, test, and provide prior information for algorithms that detect, segment, measure, and classify brain structures. Human MRI shape databases have been restricted in the past to measures of labeled region volumes and cortical region thicknesses. These measures are useful for studies of neurogenesis or atrophy in morphological development, degeneration, and disease progression. However, more subtle shape measures may help us to relate structures to behaviors or phenotypes beyond gender, handedness, and relatedness, and have great potential for use in biomarker discovery for clinical diagnosis.

As a part of the Mindboggle project, we have created software to extract brain features (labeled regions, sulci, and fundi), and compute shape measures on these features. Currently our shape measures include: mean, Gaussian, maximum, minimum, and principal directions of curvature, travel depth [1], surface area, volume, Laplace-Beltrami spectra [2], and FreeSurfer software-derived measures of depth and thickness [3]. The recent release of our Mindboggle-101 dataset (http://mindboggle.info/data), the largest and most complete set of free, publicly accessible, manually labeled human brain images [4], gives us an unprecedented opportunity to combine automated feature extraction and shape analysis to a large, manually labeled brain MRI dataset. We will present our findings on these shape measures across 101 healthy brains.

Acknowledgements

This work was funded by the NIMH R01 grant MH084029 (“Mindboggling shape analysis and identification”).

References

[1] Giard, J., Alface, P.R., and Macq. 2011. Fast surface-based travel depth estimation algorithm for macromolecule surface shape description. IEEE-ACM Transactions on Computational Biology and Bioinformatics. 8(1): 59-68.
[2] Reuter, M., Wolter, F.-E., and Peinecke, N. 2006. Laplace-Beltrami spectra as "Shape-DNA" of surfaces and solids. Computer-Aided Design. 38(4):342-366.
[3] Fischl, B., and Dale, A.M. 2000. Measuring the Thickness of the Human Cerebral Cortex from Magnetic Resonance Images. Proceedings of the National Academy of Sciences. 97:11044-11049.
[4] Klein, A, Tourville, J. 2012. 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in Brain Imaging Methods. 6:171. DOI: 10.3389/fnins.2012.00171

Keywords: morphometry, MRI, feature extraction, Shape Analysis, Mindboggle

Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.

Presentation Type: Poster

Topic: General neuroinformatics

Citation: Klein A, Giard J, Bao FS, Reuter M, Stavsky E, Hame Y, Nichols BN, Ghosh SS and Tourville J (2013). Large-scale shape analysis of human brain MRI data. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00076

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Received: 08 Apr 2013; Published Online: 11 Jul 2013.

* Correspondence: Dr. Arno Klein, Stony Brook University, Psychiatry & Behavioral Science, Stony Brook, New York, 11794-8101, United States, binarybottle@gmail.com