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Proc SPIE Int Soc Opt Eng. 2017 Feb;10137. pii: 101371D. doi: 10.1117/12.2255640. Epub 2017 Mar 13.

Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images.

Author information

1
Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
2
Dept. of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
3
Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, MD 20817, USA.
4
Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, 20892, USA.

Abstract

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

KEYWORDS:

Magnetic resonance imaging; falx cerebri; segmentation; tentorium cerebelli

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