  Statistical shape analysis is a powerful tool to assess organ morphologies
and find shape changes associated to a particular disease. However, imbalance
in confounding factors, such as demographics might invalidate the analysis if
not taken into consideration. Despite the methodological advances in the field,
providing new methods that are able to capture complex and regional shape
differences, the relationship between non-imaging information and shape
variability has been overlooked. We present a linear statistical shape analysis
framework that finds shape differences unassociated to a controlled set of
confounding variables. It includes two confounding correction methods:
confounding deflation and adjustment. We applied our framework to a cardiac
magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89
triathletes and 77 controls, to identify cardiac remodelling due to the
practice of endurance exercise. To test robustness to confounders, subsets of
this dataset were generated by randomly removing controls with low body mass
index, thus introducing imbalance. The analysis of the whole dataset indicates
an increase of ventricular volumes and myocardial mass in athletes, which is
consistent with the clinical literature. However, when confounders are not
taken into consideration no increase of myocardial mass is found. Using the
downsampled datasets, we find that confounder adjustment methods are needed to
find the real remodelling patterns in imbalanced datasets.
