To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planning 4DCT data. Tumor positions were selected on respective DRR images to place the GTV center of gravity in the center of each DRR. DRR subimages around the tumor regions were cropped so that the subimage size was defined by tumor size. Training data were then classified into two groups: positive (including tumor) and negative (not including tumor) samples. Machine learning parameters were optimized by the extremely randomized tree method. For the tracking stage, a machine learning algorithm was generated to provide a tumor likelihood map using fluoroscopic images. Prior probability tumor positions were also calculated using the previous two frames. Tumor position was then estimated by calculating maximum probability on the tumor likelihood map and prior probability tumor positions. We acquired treatment planning 4DCT images in eight patients. Digital fluoroscopic imaging systems on either side of the vertical irradiation port allowed fluoroscopic image acquisition during treatment delivery. Each fluoroscopic dataset was acquired at 15 frames per second. We evaluated the tracking accuracy and computation times. Tracking positional accuracy averaged over all patients was 1.03 ± 0.34 mm (mean ± standard deviation, Euclidean distance) and 1.76 ± 0.71 mm ([Formula: see text] percentile). Computation time was 28.66 ± 1.89 ms/frame averaged over all frames. Our markerless algorithm successfully estimated tumor position in real time.

中文翻译：

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基于机器学习的实时肿瘤跟踪系统，用于肺部放射治疗的荧光镜门控。

为了提高呼吸门放射疗法的准确性，我们开发了一种用于无标记肿瘤追踪的机器学习方法，并使用肺癌患者的数据对其进行了评估。使用计划的4DCT数据生成数字重建的射线照相（DRR）数据集。在各个DRR图像上选择肿瘤位置，以将GTV重心置于每个DRR的中心。裁剪肿瘤区域周围的DRR子图像，以使子图像大小由肿瘤大小定义。然后将训练数据分为两组：阳性（包括肿瘤）和阴性（不包括肿瘤）样本。机器学习参数通过极大随机树方法进行了优化。在跟踪阶段，生成了机器学习算法，以使用荧光镜图像提供肿瘤可能性图。还使用前两个帧计算了先前的概率肿瘤位置。然后通过在肿瘤可能性图和先前概率肿瘤位置上计算最大概率来估计肿瘤位置。我们获得了八名患者的治疗计划4DCT图像。垂直照射口两侧的数字荧光透视成像系统允许在治疗过程中进行荧光透视图像采集。每个透视数据集以每秒15帧的速度获取。我们评估了跟踪精度和计算时间。所有患者的平均追踪定位精度为1.03±0.34 mm（平均值±标准偏差，欧几里德距离）和1.76±0.71 mm（百分位数）。所有帧的平均计算时间为28.66±1.89 ms /帧。