# Statistical modeling of pneumothorax deformation by mapping CT and cone-beam CT images

arXiv: https://arxiv.org/abs/2012.13237 · HTML: https://mlsanity.com/paper/2012.13237/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks](https://mlsanity.com/paper/2102.12505/) (2021)

## Abstract (arXiv)

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.