Combining morphological information in a manifold learning framework: application to neonatal MRI.
MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.
Aljabar
P
P
Department of Computing, Imperial College London, UK. Paul.Aljabar@Imperial.ac.uk
Wolz
R
R
Srinivasan
L
L
Counsell
S
S
Boardman
J P
JP
Murgasova
M
M
Doria
V
V
Rutherford
M A
MA
Edwards
A D
AD
Hajnal
J V
JV
Rueckert
D
D
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011296:Prenatal Diagnosis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity