Ting Lai-Lei, Guo Ming-Lu, Liao Ai-Ho, Cheng Sen-Ting, Yu Hsiao-Wei, Ramanathan Subramaninan, Zhou Hong, Boominathan Catherin Meena, Jeng Shiu-Chen, Chiou Jeng-Fong, Kuo Chia-Chun, Chuang Ho-Chiao
Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan.
Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan.
Quant Imaging Med Surg. 2023 Oct 1;13(10):6827-6839. doi: 10.21037/qims-23-23. Epub 2023 Sep 5.
For respiration induced tumor displacement during a radiation therapy, a common method to prevent the extra radiation is image-guided radiation therapy. Moreover, mask region-based convolutional neural networks (Mask R-CNN) is one of the state-of-the-art (SOTA) object detection frameworks capable of conducting object classification, localization, and pixel-level instance segmentation.
We developed a novel ultrasound image tracking technology based on Mask R-CNN for stable tracking of the detected diaphragm motion and applied to the respiratory motion compensation system (RMCS). For training Mask R-CNN, 1800 ultrasonic images of the human diaphragm are collected. Subsequently, an ultrasonic image tracking algorithm was developed to compute the mean pixel coordinates of the diaphragm detected by Mask R-CNN. These calculated coordinates are then utilized by the RMCS for compensation purposes. The tracking similarity verification experiment of mask ultrasonic imaging tracking algorithm (M-UITA) is performed.
The correlation between the input signal and the signal tracked by M-UITA was evaluated during the experiment. The average discrete Fréchet distance was less than 4 mm. Subsequently, a respiratory displacement compensation experiment was conducted. The proposed method was compared to UITA, and the compensation rates of three different respiratory signals were calculated and compared. The experimental results showed that the proposed method achieved a 6.22% improvement in compensation rate compared to UITA.
This study introduces a novel method called M-UITA, which offers high tracking precision and excellent stability for monitoring diaphragm movement. Additionally, it eliminates the need for manual parameter adjustments during operation, which is an added advantage.
对于放射治疗期间呼吸引起的肿瘤移位，一种防止额外辐射的常用方法是图像引导放射治疗。此外，基于掩码区域的卷积神经网络（Mask R-CNN）是能够进行目标分类、定位和像素级实例分割的最先进（SOTA）目标检测框架之一。
我们开发了一种基于Mask R-CNN的新型超声图像跟踪技术，用于稳定跟踪检测到的膈肌运动，并将其应用于呼吸运动补偿系统（RMCS）。为了训练Mask R-CNN，收集了1800张人体膈肌的超声图像。随后，开发了一种超声图像跟踪算法，以计算由Mask R-CNN检测到的膈肌的平均像素坐标。然后，RMCS利用这些计算出的坐标进行补偿。进行了掩码超声成像跟踪算法（M-UITA）的跟踪相似性验证实验。
在实验过程中评估了输入信号与M-UITA跟踪的信号之间的相关性。平均离散弗雷歇距离小于4毫米。随后，进行了呼吸位移补偿实验。将所提出的方法与UITA进行比较，并计算和比较了三种不同呼吸信号的补偿率。实验结果表明，与UITA相比，所提出的方法在补偿率上提高了6.22%。
本研究介绍了一种名为M-UITA的新方法，该方法在监测膈肌运动方面具有高跟踪精度和出色的稳定性。此外，它消除了操作过程中手动调整参数的需要，这是一个额外的优势。
