Comparison of default patient surface model estimation methods

Abstract

A patient model is useful for many clinical applications such as patient positioning, device placement, or dose estimation in case of X-ray imaging. A default or a-priori patient model can be estimated using learning based methods trained over a large database. Different methods can be used to estimate such a default model given a restricted number of the input parameters. We investigated different learning based estimation strategies using patient gender, height, and weight as the input to estimate a default patient surface model. We implemented linear regression, an active shape model, kernel principal component analysis and a deep neural network method. These methods are trained on a database containing about 2000 surface meshes. Using linear regression, we obtained a mean vertex error of 20.8±14.7 mm for men and 17.8±11.6 mm for women, respectively. While the active shape model and kernel PCA method performed better than linear regression, the results also revealed that the deep neural network outperformed all other methods with a mean vertex error of 15.6±9.5 mm for male and 14.5±9.3 mm for female models.