| 11th Annual OHBM Meeting | ||
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| Abstract Number: 633 |
Submitted By: Aaron Kline | |
| Last Modified: 11 Jan 05 | ||
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Department of Mathematics, Florida State University, Tallahassee, U.S.A., 32306-4510 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Objective: Increasingly, brain
cartographic methods are being implemented in attempts to
create cortical surface reconstructions that are useful and
accurate. Aesthetically pleasing images of the human cortical
surface have been rendered, but beyond visual appeal, these
surfaces need to be characteristically accurate. Creating
characteristically accurate surfaces is a nontrivial
objective, for they must be topologically correct and
accurately represent cerebral anatomy. A comparison of the
characteristics of the surfaces created by packages such as
FreeSurfer[1], BrainVISA[2], and others will aide in
identifying strengths of the different software packages.
Methods: We are evaluating cortical surface reconstruction methods contained in a number of software packages that are currently available to the neuroscience community. Several largely automated algorithms have been implemented in an attempt to create accurate human cortical surface reconstructions, three of which are FreeSurfer, BrainVISA and INCSurf[3]. We have created surfaces representing the grey and white matter using these different methods on magnetic resonance imaging (MRI) data from 11 different subjects. We are examining a number of different surface characteristics of the cerebral hemisphere, including surface area, sulcal length, and maximum volume bound of the cortex. The maximum volume bound represents the maximum volume of the cortex if the sulci were "filled" in. Results & Discussion: Preliminary attributes of the surfaces, such as surface area, vary considerably in the surfaces of single subjects across methods (see Table 1). Conclusions: Studying and quantifying different cortical surface reconstruction algorithms is an important issue for neuromorphometric analysis. It is critical to understand the biases, differences, and similarities in the resulting surfaces produced from different algorithms in this study. These validated surface reconstructions will aide in more localized and accurate identification of cerebral processing, opening the door for more insightful neurological studies. References & Acknowledgements: [1] Dale, A. M., Fischl, B., and Sereno, M. I., Cortical Surface-based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage, vol. 9: 179-194, 1999. FreeSurfer available at http://surfer.nemr.mgh.harvard.edu/ [2] Mangin, J. F., Riviere, D., Cachia, A., Duchesnay, E., Cointepas, Y., Papadopoulos-Orfanos, D., Collins, D. L., Evans, A. C., Regis, J., Object-based Morphometry of the Cerebral Cortex. IEEE Trans. Medical Imaging, 23(8):968-982, Aug. 2004. BrainVISA available at http://brainvisa.info/ [3] Stern, J., Schaper, K., Rehm, K., Hurdal, M. K., Sumners, De Witt, and Rottenberg, D. A., Automatic Surface Extraction by Discrete, Topolgically-controlled, Region Growing. NeuroImage, vol. 13, pg. S44, 2001. This work is supported in part by NSF grant DMS-0101329 and NIH grant P20 EB02013. We would like to thank Dr. David Rottenberg, Departments of Radiology and Neurology, University of Minnesota for providing the MRI data.
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