Interpretability of anatomical variability analysis of abdominal organs via clusterization of decomposition modes.
Extensive recent work has taken place on the construction of probabilistic atlases of anatomical organs, especially the brain, and their application in medical image analysis. These techniques are leading the way into similar studies of other organs and more comprehensively of groups of organs. In this paper we report results on the analysis of anatomical variability obtained from probabilistic atlases of abdominal organs. Two factor analysis techniques, namely principal component analysis (PCA) and principal factor analysis (PFA), were used to decompose and study shape variability within the abdomen. To assess and ease the interpretability of the resulting deformation modes, a clustering technique of the deformation vectors is proposed. The analysis of deformation fields obtained using these two factor analysis techniques showed strong correlation with anatomical landmarks and known mechanical deformations in the abdomen, allowing us to conclude that PFA is a complementary decomposition technique that offers easy-to-interpret additional information to PCA in a clinical setting. The analysis of organ anatomical variability will represent a potentially important research tool for abdominal diagnosis and modeling.
Reyes
Mauricio
M
Institute of Surgical Technologies and Biomechanics, MEM Research Center, University Bern, Switzerland. mauricio.reyes@memcenter.unibe.ch
Gonzalez Ballester
Miguel A
MA
Li
Zhixi
Z
Kozic
Nina
N
Summers
Ronald M
RM
Linguraru
Marius George
MG
D000886:Anthropometry / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014781:Viscera / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*