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Multi-atlas-based fully automatic segmentation of individual muscles in rat leg

  • Research Article
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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

To quantify individual muscle volume in rat leg MR images using a fully automatic multi-atlas-based segmentation method.

Materials and methods

We optimized a multi-atlas-based segmentation method to take into account the voxel anisotropy of numbers of MRI acquisition protocols. We mainly tested an image upsampling process along Z and a constraint on the nonlinear deformation in the XY plane. We also evaluated a weighted vote procedure and an original implementation of an artificial atlas addition. Using this approach, we measured gastrocnemius and plantaris muscle volumes and compared the results with manual segmentation. The method reliability for volume quantification was evaluated using the relative overlap index.

Results

The most accurate segmentation was obtained using a nonlinear registration constrained in the XY plane by zeroing the Z component of the displacement and a weighted vote procedure for both muscles regardless of the number of atlases. The performance of the automatic segmentation and the corresponding volume quantification outperformed the interoperator variability using a minimum of three original atlases.

Conclusion

We demonstrated the reliability of a multi-atlas segmentation approach for the automatic segmentation and volume quantification of individual muscles in rat leg and found that constraining the registration in plane significantly improved the results.

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Abbreviations

3D:

Standard 3D nonlinear registration process

SBS:

Slice-by-slice nonlinear registration process

2Dc:

2D constrained nonlinear registration process

R-:

Rescaled along Z

RO:

Relative overlap index

CV:

Coefficient of variation

ICC:

Intraclass coefficient

mv:

Majority vote procedure

wv:

Weighted vote procedure

z0:

No slice translation

z1:

1 slice translation

z2:

2 slice translation

DSC:

Dice similarity coefficient

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Author’s contribution

Michael Sdika: project development, data management and data analysis; Anne Tonson: project development, data collection and data analysis; Yann Le Fur: data collection; Patrick J. Cozzone: data analysis; David Bendahan: data analysis.

Author information

Author notes

  1. Anne Tonson

    Present address: Department of Physiology, Michigan State University, East Lansing, MI, 48824, USA

Authors and Affiliations

  1. Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), Centre de Résonance Magnétique Biologique et Médicale (CRMBM) UMR 7339, 13385, Marseille, France

    Michael Sdika, Anne Tonson, Yann Le Fur, Patrick J. Cozzone & David Bendahan

  2. Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, 7 Avenue Jean Capelle, 69621, Villeurbanne Cedex, France

    Michael Sdika

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  1. Michael Sdika
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Corresponding author

Correspondence to Michael Sdika.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

All procedures involving animals were performed according to the guidelines of the National Research Council Guide for the care and use of laboratory animals and the French law on the Protection of Animals.

Additional information

Michael Sdika and Anne Tonson have contributed equally to this work.

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Sdika, M., Tonson, A., Le Fur, Y. et al. Multi-atlas-based fully automatic segmentation of individual muscles in rat leg. Magn Reson Mater Phy 29, 223–235 (2016). https://doi.org/10.1007/s10334-015-0511-6

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  • DOI: https://doi.org/10.1007/s10334-015-0511-6

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