Wang Xiaoyu, Liu Tianbo, Mai Songping
Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518071 Guangdong China.
Biomed Eng Lett. 2024 May 19;14(5):1057-1068. doi: 10.1007/s13534-024-00390-3. eCollection 2024 Sep.
The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon's reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584 on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient's thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.
传统的肺部穿刺手术操作复杂，这是因为外科医生在患者屏气时依赖对呼吸状况的视觉评估以及手动执行该技术。然而，穿刺技术执行不当可能会导致不良后果，如出现溃疡和气胸。在这项工作中，我们提出了一种利用缺陷感知点云配准和描述符计算来监测呼吸运动的新方法。通过全面检查输入的属性，我们建议在配准网络中加入缺陷检测分支。此外，我们开发了两个模块，旨在提高提取特征的质量。为呼吸运动跟踪设计了一种基于描述符计算的由粗到精的呼吸阶段识别方法。通过在公开可用数据集和胸腹点云数据集上进行的实验结果证明了所建议配准方法的有效性。我们在ModelNet40数据集上获得了领先的配准结果，旋转平均绝对误差为1.584，平移平均绝对误差为0.016毫米。在胸腹点云数据集上进行的实验结果表明，我们的方法具有有效性和高效性，实现了每秒2帧的帧匹配率和96.3%的阶段识别准确率。这使得能够从显示患者胸腹表面不同部位的模板点云中识别匹配帧，同时在正常呼吸时区分呼吸阶段并跟踪呼吸。
