  Objective: A digital twin of a patient can be a valuable tool for enhancing
clinical tasks such as workflow automation, patient-specific X-ray dose
optimization, markerless tracking, positioning, and navigation assistance in
image-guided interventions. However, it is crucial that the patient's surface
and internal organs are of high quality for any pose and shape estimates. At
present, the majority of statistical shape models (SSMs) are restricted to a
small number of organs or bones or do not adequately represent the general
population. Method: To address this, we propose a deformable human shape and
pose model that combines skin, internal organs, and bones, learned from CT
images. By modeling the statistical variations in a pose-normalized space using
probabilistic PCA while also preserving joint kinematics, our approach offers a
holistic representation of the body that can benefit various medical
applications. Results: We assessed our model's performance on a registered
dataset, utilizing the unified shape space, and noted an average error of 3.6
mm for bones and 8.8 mm for organs. To further verify our findings, we
conducted additional tests on publicly available datasets with multi-part
segmentations, which confirmed the effectiveness of our model. Conclusion: This
works shows that anatomically parameterized statistical shape models can be
created accurately and in a computationally efficient manner. Significance: The
proposed approach enables the construction of shape models that can be directly
applied to various medical applications, including biomechanics and
reconstruction.
