Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Sensors (Basel). 2022 Apr 11;22(8):2918. doi: 10.3390/s22082918.
This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.
本文提出了一种基于时间序列深度学习的 3D Kinect 相机方案，用于对带有肺部肿瘤的呼吸阶段进行分类，并预测肺部肿瘤的位移。具体来说，该方案由两个基于时间序列深度学习的算法模型驱动：呼吸阶段分类模型和基于回归的预测模型。为了评估所提出方案的性能，使用四类数据集对分类和预测模型进行了测试：具有规则和不规则呼吸模式的基于患者的数据集，以及具有规则和不规则呼吸模式的基于伪患者的数据集。在本研究中，“伪患者”是指带有肺部肿瘤的动态胸腔模拟体，该肿瘤具有不同的呼吸模式和每分钟呼吸次数。对于四类数据集，呼吸阶段分类模型的总准确率为 100%、100%、100%和 92.44%，对应的均方误差 (MSE)、平均绝对误差 (MAE) 和决定系数 (R) 分别为 1.2-1.6%、0.65-0.8%和 0.97-0.98。结果表明，基于时间序列深度学习的分类和回归预测模型可以以高精度对呼吸阶段进行分类，并预测肺部肿瘤的位移。本质上，本研究的新颖之处在于在医疗领域中使用低成本的 3D Kinect 相机和基于时间序列深度学习的算法来高效地对呼吸阶段进行分类和预测肺部肿瘤的位移。
