摘要
目的探索基于深度学习实现丘脑底核脑深部电刺激术(subthalamic nucleus deep brain stimulation,STN-DBS)自动化核团标注、手术靶点定位及电极植入路径规划。方法来自北部战区总医院神经外科行双侧STN-DBS手术的155例患者的影像资料。首先,采用3D UX Net卷积神经网络基于MRI图像提取深度学习特征并完成丘脑底核(subthalamic nucleus,STN)及红核(red nuclei,RN)的分割;然后通过算法实现丘脑底核的自动化靶点定位;最后在规定的皮层入路点范围区域内生成不多于4根的电极植入路径。通过人工审查验证靶点及电极植入路径的临床可行性。结果通过3D UX Net卷积神经网络算法进行核团分割,红核分割性能的Dice值为0.90。STN分割性能的Dice值为0.84。自动化生成STN靶点经人工审查验证可行的坐标与人工手术规划STN靶点坐标的欧几里得距离为(1.2±0.4)mm。25例测试集中自动化STN靶点定位及电极植入路径通过人工审查可行有20例(20/25),自动化靶点定位及电极路径与人工规划靶点及路径差异无统计学意义(P=0.059)。结论3D UX Net卷积神经网络可以较精确地分割STN及红核,通过自动化靶点定位及路径规划的STN-DBS手术计划临床可行且耗时短,可以为临床医生手术前规划提供参考。
Objective To make automated brain nucleus labeling,surgical target localization,and electrode implantation trajectory planning for subthalamic nucleus deep brain stimulation(STN-DBS)based on deep learning.Methods The imaging data of 155 patients receiving bilateral STN-DBS surgery at the Department of Neurosurgery,General Hospital of the Northern Theater Command were analyzed in the following three steps.First,a 3D UX Net convolutional neural network was used to extract deep learning features from MRI images and complete the segmentation of subthalamic nucleus(STN)and red nuclei(RN).Secondly,STN target localization was achieved through algorithms.Finally,no more than 4 electrode implantation trajectories were generated within the specified range area.Clinical feasibility of the target and electrode implantation trajectories were verified through manual review.Results The mean Dice coefficient of RN and STN was 0.90 and 0.84 respectively.The Euclidean distance between the coordinates of the automated STN target,which have been verified to be feasible through manual review,and the coordinates of the STN target in manual surgical planning was 1.2±0.4 mm.25 cases of the automated STN target localization and electrode implantation trajectory in the test set were reviewed manually,and 20 of them(20/25)were feasible.There was no statistically significant difference in automated target localization and electrode implantation trajectory compared to manually planned target and trajectory(P=0.059).Conclusion The 3D UX Net convolutional neural network can be used to segment STN and RN accurately,as the results of the automated target localization and trajectory planning are clinically feasible and can save time,so it can provide reference for clinical doctors in preoperative planning.
作者
吴伟东
巩顺
李欣阳
陶英群
WU Weidong;GONG Shun;LI Xinyang;TAO Yingqun(Department of Neurosurgery,General Hospital of the Northern Theater Command,Shenyang,Liaoning 110016,China;China Medical University,Shenyang,Liaoning 110122,China;Liaoning Kangmao Medical Technology Co.,Ltd,Shenyang,Liaoning 110004,China)
出处
《中华神经外科疾病研究杂志》
CAS
2024年第4期37-43,共7页
Chinese Journal of Neurosurgical Disease Research
基金
辽宁省应用基础研究计划(2022JH2/101300055、2023JH2/101700104)。
关键词
深度学习
自动化
脑深部电刺激术
手术规划
Deep learning
Automation
Deep brain stimulation
Surgical planning
