Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications

Frontiers in Neuroscience

Seonhwa Lee¹  Chong Sik Lee²  Wooyoung Jang³  Jung Kyo Lee⁴  Seong-Cheol Park⁵  Joon Hyuk Cha⁷ 

[1] Department of Bio-Convergence Engineering, College of Health Science, Korea University, Seoul, South Korea;Department of Neurology, Asan Medical Center, University of Ulsan, Seoul, South Korea;Department of Neurology, Gangneung Asan Hospital, University of Ulsan, Gangneung, South Korea;Department of Neurosurgery, Asan Medical Center, University of Ulsan, Seoul, South Korea;Department of Neurosurgery, Gangneung Asan Hospital, University of Ulsan, Gangneung, South Korea;Department of Neurosurgery, Seoul Metropolitan Government – Seoul National University Boramae Medical Center, Seoul, South Korea;School of Medicine, Inha University, Incheon, South Korea;

关键词: deep learning;    deep brain stimulation;    convolutional neural network;    semantic segmentation;    clinical application;   

DOI  :  10.3389/fnins.2019.01128

来源: DOAJ

【 摘 要 】

BackgroundThe 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.MethodsT2∗ 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 ∼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.ResultsMean 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.ConclusionThe 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.

【 授权许可】

Unknown