title: Multi-atlas-based fully automatic segmentation of individual muscles in rat leg.
abstract: OBJECTIVE
  To quantify individual muscle volume in rat leg MR images using a fully automatic multi-atlas-based segmentation method.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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@creatis.insa-lyon.fr.|Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, 7 Avenue Jean Capelle, 69621, Villeurbanne Cedex, France. michael.sdika@creatis.insa-lyon.fr.
authors.forename: Michael
authors.initials: M
authors.lastname: Sdika
authors.affiliation: Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), Centre de Résonance Magnétique Biologique et Médicale (CRMBM) UMR 7339, 13385, Marseille, France.|Department of Physiology, Michigan State University, East Lansing, MI, 48824, USA.
authors.forename: Anne
authors.initials: A
authors.lastname: Tonson
authors.affiliation: Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), Centre de Résonance Magnétique Biologique et Médicale (CRMBM) UMR 7339, 13385, Marseille, France.
authors.forename: Yann
authors.initials: Y
authors.lastname: Le Fur
authors.affiliation: Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), Centre de Résonance Magnétique Biologique et Médicale (CRMBM) UMR 7339, 13385, Marseille, France.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Cozzone
authors.affiliation: Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), Centre de Résonance Magnétique Biologique et Médicale (CRMBM) UMR 7339, 13385, Marseille, France.
authors.forename: David
authors.initials: D
authors.lastname: Bendahan
mesh_terms: D000465:Algorithms; D000818:Animals; D005260:Female; D006614:Hindlimb / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D018482:Muscle, Skeletal / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D017208:Rats, Wistar; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Anisotropy; Magnetic resonance imaging; Multi-atlas; Rat; Reproducibility of results; Segmentation; Skeletal muscle