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

Utako Yamamoto, Megumi Nakao, Masayuki Ohzeki, Junko Tokuno, Toyofumi Fengshi Chen-Yoshikawa, Tetsuya Matsuda

研究成果: Article査読


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 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 achieved the estimation lung deformations from a small number of training cases and a small observation area.This study contributes to data-driven modeling of pneumothorax deformation of the lung.

ジャーナルExpert Systems with Applications
出版ステータスPublished - 2021 11月 30

ASJC Scopus subject areas

  • 工学(全般)
  • コンピュータ サイエンスの応用
  • 人工知能


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