College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, China.
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Int J Comput Assist Radiol Surg. 2021 May;16(5):809-818. doi: 10.1007/s11548-021-02377-2. Epub 2021 Apr 27.
Microelectrode recordings (MERs) are a significant clinical indicator for sweet spots identification of implanted electrodes during deep brain stimulation of the subthalamic nucleus (STN) surgery. As 1D MERs signals have the unboundedness, large-range, large-amount and time-dependent characteristics, the purpose of this study is to propose an automatic and precise identification method of sweet spots from MERs, reducing the time-consuming and labor-intensive human annotations.
We propose an automatic identification method of sweet spots from MERs for electrodes implantation in STN-DBS. To better imitate the surgeons' observation and obtain more intuitive contextual information, we first employ the 2D Gramian angular summation field (GASF) images generated from MERs data to perform the sweet spots determination for electrodes implantation. Then, we introduce the convolutional block attention module into convolutional neural network (CNN) to identify the 2D GASF images of sweet spots for electrodes implantation.
Experimental results illustrate that the identification result of our method is consistent with the result of doctor's decision, while our method can achieve the accuracy and precision of 96.72% and 98.97%, respectively, which outperforms state-of-the-art for intraoperative sweet spots determination.
The proposed method is the first time to automatically and accurately identify sweet spots from MERs for electrodes implantation by the combination an advanced time series-to-image encoding way with CBAM-enhanced networks model. Our method can assist neurosurgeons in automatically detecting the most likely locations of sweet spots for electrodes implantation, which can provide an important indicator for target selection while it reduces the localization error of the target during STN-DBS surgery.
微电极记录 (MERs) 是深部脑刺激丘脑底核 (STN) 手术中植入电极时识别甜区的重要临床指标。由于 1D MERs 信号具有无界性、大范围、大量和时变性，本研究旨在提出一种从 MERs 中自动、精确识别甜区的方法，减少耗时且费力的人工标注。
我们提出了一种用于 STN-DBS 中电极植入的从 MERs 中自动识别甜区的方法。为了更好地模拟外科医生的观察并获得更直观的上下文信息，我们首先使用从 MERs 数据生成的 2D Gramian 角总和场 (GASF) 图像来进行电极植入的甜区确定。然后，我们将卷积块注意力模块引入卷积神经网络 (CNN) 中，以识别电极植入的 2D GASF 图像的甜区。
实验结果表明，我们的方法的识别结果与医生的决策结果一致，而我们的方法可以分别达到 96.72%和 98.97%的准确率和精度，优于术中甜区确定的最新技术。
该方法首次通过先进的时间序列到图像编码方法与 CBAM 增强网络模型相结合，实现了从 MERs 中自动、准确地识别电极植入的甜区。我们的方法可以帮助神经外科医生自动检测电极植入的甜区最可能的位置，从而为目标选择提供重要指标，并减少 STN-DBS 手术中目标的定位误差。
