  In this study, we introduce statistical modeling methods for pneumothorax
deformation using paired cone-beam computed tomography (CT) images. We designed
a deformable mesh registration framework for shape changes involving non-linear
deformation and rotation of the lungs. The registered meshes with local
correspondences are available for both surgical guidance in thoracoscopic
surgery and building statistical deformation models with inter-patient
variations. In addition, a kernel-based deformation learning framework is
proposed to reconstruct intraoperative deflated states of the lung from the
preoperative CT models. This paper reports the findings of pneumothorax
deformation and evaluation results of the kernel-based deformation framework.
