Statistical shape analysis of the human spleen geometry for probabilistic occupant models

Abstract

Statistical shape models are an effective way to create computational models of human organs that can incorporate inter-subject geometrical variation. The main objective of this study was to create statistical mean and boundary models of the human spleen in an occupant posture. Principal component analysis was applied to fifteen human spleens in order to find the statistical modes of variation, mean shape, and boundary models. A landmark sliding approach was utilized to refine the landmarks to obtain a better shape correspondence and create a better representation of the underlying shape contour. The first mode of variation was found to be the overall volume, and it accounted for 69% of the total variation. The mean model and boundary models could be used to develop probabilistic finite element (FE) models which may identify the risk of spleen injury during vehicle collisions and consequently help to improve automobile safety systems.