# Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks

arXiv: https://arxiv.org/abs/2102.12505 · HTML: https://mlsanity.com/paper/2102.12505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration](https://mlsanity.com/paper/1807.00467/) (2018)
- [Learning Soft Tissue Behavior of Organs for Surgical Navigation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.00722/) (2019)
- [Deformation estimation of an elastic object by partial observation using a neural network](https://mlsanity.com/paper/1711.10157/) (2017)
- [Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields](https://mlsanity.com/paper/1902.09328/) (2019)
- [Statistical modeling of pneumothorax deformation by mapping CT and cone-beam CT images](https://mlsanity.com/paper/2012.13237/) (2020)

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

- [A Review on Organ Deformation Modeling Approaches for Reliable Surgical Navigation using Augmented Reality](https://mlsanity.com/paper/2408.02713/) (2024)

## Abstract (arXiv)

In video-assisted thoracoscopic surgeries, successful procedures of nodule resection are highly dependent on the precise estimation of lung deformation between the inflated lung in the computed tomography (CT) images during preoperative planning and the deflated lung in the treatment views during surgery. Lungs in the pneumothorax state during surgery have a large volume change from normal lungs, making it difficult to build a mechanical model. The purpose of this study is to develop a deformation estimation method of the 3D surface of a deflated lung from a few partial observations. To estimate deformations for a largely deformed lung, a kernel regression-based solution was introduced. The proposed method used a few landmarks to capture the partial deformation between the 3D surface mesh obtained from preoperative CT and the intraoperative anatomical positions. The deformation for each vertex of the entire mesh model was estimated per-vertex as a relative position from the landmarks. The landmarks were placed in the anatomical position of the lung's outer contour. The method was applied on nine datasets of the left lungs of live Beagle dogs. Contrast-enhanced CT images of the lungs were acquired. The proposed method achieved a local positional error of vertices of 2.74 mm, Hausdorff distance of 6.11 mm, and Dice similarity coefficient of 0.94. Moreover, the proposed method could estimate lung deformations from a small number of training cases and a small observation area. This study contributes to the data-driven modeling of pneumothorax deformation of the lung.