Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks
Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks
复制标题
基于内核的框架使用解剖标志的相对位置估计气胸肺的变形
DOI：
10.1016/j.eswa.2021.115288
复制
发表时间：
2021
影响因子：
8.5
通讯作者：
Matsuda Tetsuya
中科院分区：
文献类型：
--
作者：
Yamamoto Utako;Nakao Megumi;Ohzeki Masayuki;Tokuno Junko;Chen-Yoshikawa Toyofumi Fengshi;Matsuda Tetsuya
在电视胸镜手术中，结节切除手术的成功与否在很大程度上依赖于对术前计划中CT图像中的充气肺和手术中治疗切面中的放空肺之间的肺变形的准确估计。手术中处于气胸状态的肺与正常肺相比，体积变化较大，很难建立力学模型，本研究的目的是通过少量局部观测，建立一种估算放气肺三维表面变形的方法。为了估计较大变形的肺的变形，引入了基于核回归的解决方案。该方法利用几个标志点来捕捉术前CT获得的三维表面网格与术中解剖位置之间的部分变形。整个网格模型的每个顶点的变形是按每个顶点估计的，作为与地标的相对位置。这些标志物被放置在肺外轮廓的解剖位置上。该方法被应用于9个活的比格犬左肺的数据集。该方法获得了肺部增强CT图像，局部定位误差为2.74 mm，Hausdorff距离为6.11 mm，Dice相似系数为0.94。此外，该方法能够在较少的训练样本和较小的观测区域内实现对肺部变形的估计，为气胸肺部变形的数据驱动建模奠定了基础。
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.