Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications
Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications
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DOI：
10.3389/fnins.2019.01128
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发表时间：
2019-10-24
影响因子：
4.3
通讯作者：
Lee, Jung Kyo
中科院分区：
文献类型：
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作者：
Park, Seong-Cheol;Cha, Joon Hyuk;Lee, Jung Kyo
本研究的目的是评价基于深度学习的脑深部电刺激（DBS）图像引导手术计划。我们开发了基于深度学习语义分割的DBS靶向，并前瞻性地将该方法应用于临床。方法采用102例患者的T2* 快速梯度回波图像进行训练和验证。为丘脑底核和红核准备手动绘制的地面实况信息，轴向切口类似于前-后连合线下方4 mm。使用全卷积神经网络（FCN-VGG-16）通过语义分割确保边缘识别。进行了9次图像对比度增强。多达102个原始图像和918个增强图像用于训练和验证。语义分割的准确性通过平均准确度和平均交集来衡量。考虑到Bejjani靶，根据靶与这些分割解剖结构的相对距离计算靶。结果当使用360张增强验证图像时，平均准确度和平均交集在联合值上很高：62张训练图像分别为0.904和0.813，558张增强训练图像分别为0.911和0.821。当使用720个训练图像和198个验证图像时，从并集上的交集转换的Dice系数为0.902。语义分割是自适应的高度解剖变化的大小，形状和不对称性。对于临床应用，评估了两名患者：一名患有原发性震颤，另一名患有帕金森病引起的运动迟缓和步态障碍。两例患者术后均无并发症，微电极记录显示后一例患者的丘脑底核信号。结论基于深度学习的语义分割的准确率可能超过以前的方法。DBS靶向及其临床应用是通过使用精确的基于深度学习的语义分割来实现的，这种分割可以适应解剖结构的变化。
Background The purpose of the present study was to evaluate deep learning-based image-guided surgical planning for deep brain stimulation (DBS). We developed deep learning semantic segmentation-based DBS targeting and prospectively applied the method clinically. Methods T2* fast gradient-echo images from 102 patients were used for training and validation. Manually drawn ground truth information was prepared for the subthalamic and red nuclei with an axial cut similar to 4 mm below the anterior-posterior commissure line. A fully convolutional neural network (FCN-VGG-16) was used to ensure margin identification by semantic segmentation. Image contrast augmentation was performed nine times. Up to 102 original images and 918 augmented images were used for training and validation. The accuracy of semantic segmentation was measured in terms of mean accuracy and mean intersection over the union. Targets were calculated based on their relative distance from these segmented anatomical structures considering the Bejjani target. Results Mean accuracies and mean intersection over the union values were high: 0.904 and 0.813, respectively, for the 62 training images, and 0.911 and 0.821, respectively, for the 558 augmented training images when 360 augmented validation images were used. The Dice coefficient converted from the intersection over the union was 0.902 when 720 training and 198 validation images were used. Semantic segmentation was adaptive to high anatomical variations in size, shape, and asymmetry. For clinical application, two patients were assessed: one with essential tremor and another with bradykinesia and gait disturbance due to Parkinson's disease. Both improved without complications after surgery, and microelectrode recordings showed subthalamic nuclei signals in the latter patient. Conclusion The accuracy of deep learning-based semantic segmentation may surpass that of previous methods. DBS targeting and its clinical application were made possible using accurate deep learning-based semantic segmentation, which is adaptive to anatomical variations.