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Brain MRI Segmentation

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Abstract

Segmentation is an important step for quantitative analysis of brain images and for the study of many brain disorders. Indeed, structural changes in the brain can be due to some brain disorders. The quantification of these changes, by measuring volumes of structures of interest, can be used to characterize disease severity or evolution. For instance, in morphometry, brain tissue segmentation enables to compare tissue volumes and to follow the evolution of some brain disorders. Before these measurements can be carried out, the labeling process must be performed. In this approach, the tissue types of interest are white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), but other approaches classify voxels according to their anatomical structure. As most studies involve large amounts of data, manual tracing of cerebral structures in MR images by a human expert is obviously a time-consuming process. Moreover, these manual segmentations are prone to large intra- and interobserver variability, which deteriorates the significance of the resulting segmentation analysis.

Thus, due to different artifacts appearing on MR images, segmentation is not obvious and remains a challenging task. The first point deals with spatial regularization required to overcome the disturbance added during the MRI formation. The second artifact that appears in MR images is the corruption by a bias field. The third important issue in MR segmentation is the partial volume effect (PVE). Due to the limited resolution of acquisition system, voxels along the boundaries are composed of two or more tissues. Therefore, it is necessary to consider this partial volume effect in order to achieve an accurate segmentation of brain tissues. In fact, hard segmentation (WM, GM, CSF) ignores this problem and therefore loses information concerning the tissue structure.

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Notes

  1. 1.

    http://www.fil.ion.ucl.ac.uk/spm.

  2. 2.

    http://www.medicalimagecomputing.com/EMS/.

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Acknowledgements

We thank the Alsace Region and the CNRS for funding this research. We thank the Computational Geometry and Computer Graphics team of the LSIIT for their graphic modeling platform.

Author information

Authors and Affiliations

  1. Image Sciences, Computer Sciences and Remote Sensing Lab. (LSIIT UMR CNRS 7005), Strasbourg University, Strasbourg, France

    Stéphanie Bricq & Christophe Collet

  2. Faculté de Médecine, LINC-IPB, 4, rue Kirschleger, F - 67085, STRASBOURG Cedex, France

    Jean-Paul Armspach

Authors
  1. Stéphanie Bricq
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  2. Christophe Collet
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  3. Jean-Paul Armspach
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Corresponding author

Correspondence to Christophe Collet .

Editor information

Editors and Affiliations

  1. Dept. Computer Science, University of Houston, Calhoun Rd. 4800, Houston, 77204-3475, U.S.A.

    Marc Garbey

  2. Methodist Hospital Research Institute, Fannin St. 6565, Houston, 77030, U.S.A.

    Barbara Lee Bass

  3. ENSPS, Université Strasbourg I, boulevard Sébastien Brant, Illkirch, 67412, France

    Christophe Collet

  4. ENSPS, Université Louis Pasteur -Strasbourg I, Pôle API Boulevard. Sébastien Brant, Illkirch, 67400, France

    Michel Mathelin

  5. Dept. Mechanical & Aerospace Engineering, University of Florida, Gainesville, 32611-6250, U.S.A.

    Roger Tran-Son-Tay

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Bricq, S., Collet, C., Armspach, JP. (2010). Brain MRI Segmentation. In: Garbey, M., Bass, B., Collet, C., Mathelin, M., Tran-Son-Tay, R. (eds) Computational Surgery and Dual Training. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1123-0_3

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  • DOI: https://doi.org/10.1007/978-1-4419-1123-0_3

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