Prediction of organ geometry from demographic and anthropometric data based on supervised learning approach using statistical shape atlas

We propose a method relating internal human organ geometries and non-invasively acquired information such as demographic and anthropometric data. We first apply a dimensionality reduction technique to a training dataset to represent the organ geometry with low dimensional feature coordinates. Regression analysis is then used to determine a regression function between feature coordinates and the external measurements of the subjects. Feature coordinates for the organ of an unknown subject are then predicted from external measurements using the regression function, subsequently the organ geometry is estimated from the feature coordinates. As an example case, lung shapes represented as a point distribution model was analyzed based on demographic (age, gender, race), and several anthropometric measurements (height, weight, and chest dimensions). The training dataset consisted of 124 topologically consistent lung shapes created from thoracic CT scans. The prediction error of lung shape of an unknown subject based on 11 demographic and anthropometric information was 10.71 ± 5.48 mm. This proposed approach is applicable to scenarios where the prediction of internal geometries from external parameters is of interest. Examples include the use of external measurements as a prior information for image quality improvement in low dose CT, and optimization of CT scanning protocol.