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首页> 外文会议>International Conference on Medical Imaging Physics and Engineering >A Feasibility Study on Markerless Real-Time Tumor Tracking Based on Faster-RCNN for Lung Cancer Radiotherapy
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A Feasibility Study on Markerless Real-Time Tumor Tracking Based on Faster-RCNN for Lung Cancer Radiotherapy

机译：基于肺癌放射疗法的rcnn的无价值实时肿瘤跟踪的可行性研究

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摘要

Traditional templates matching methods have achieved good accuracy in markerless tumor tracking (MTT) on a Linac with the on-board imaging system. Nevertheless, its computational complexity hinders us from marching towards real-time tracking. Therefore, we aimed at evaluating both the accuracy and speed of our novel MTT method using Faster-RCNN, which is the most sophisticated RCNN based model trained via supervised learning. To this end, we use a dynamic thorax phantom that simulates the human chest anatomy and tumor motion in the lung. To feed the network model enough data, we generate digitally reconstructed radiography (DRR) from clinically used four-dimensional(4D) CT as the training dataset. Restricted by this, fluoroscopy images are preprocessed by histogram matching to ensure the maximum similarity between training dataset and testing dataset. Tracking errors were 0.92mm, 0.85mm, and 0.43mm correspond to target diameters of 12mm, 18mm, 27mm, respectively. The average end to end latency was around 220ms. In conclusion, the results verified that our proposed method was qualified for real-time MTT in the case of phantom study for its balance of accuracy and speed.
机译：传统的模板匹配方法在LinaC与板载成像系统上的无价值肿瘤跟踪（MTT）取得了良好的准确性。尽管如此，其计算复杂性阻碍了我们走向实时跟踪。因此，我们旨在使用更快的RCNN评估我们的新型MTT方法的准确性和速度，这是通过监督学习培训的最复杂的基于RCNN的模型。为此，我们使用动态胸部幻影，用于在肺部中模拟人类胸部解剖和肿瘤运动。为了提供足够的数据，我们从临床使用的四维（4D）CT为训练数据集，我们生成数字重建的射线照相（DRR）。受此限制，通过直方图匹配预处理透视图像以确保训练数据集和测试数据集之间的最大相似性。跟踪误差为0.92mm，0.85mm，0.43mm，分别对应于12mm，18mm，27mm的目标直径。最终延迟的平均端约为220ms。总之，结果证实，在幻影研究的情况下，我们所提出的方法是在实时MTT的实时MTT，以实现精度和速度平衡。

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