Accurate preoperative path planning with coarse-to-refine segmentation for image guided deep brain stimulation. (September 2022) - Cai, Bin; Xiong, Chi; Sun, Zhiyong; Liang, Pengpeng; Wang, Kaifeng; Guo, Yuhao; Niu, Chaoshi; Song, Bo; Cheng, Erkang; Luo, Xiongbiao
Abstract: Accurate preoperative path planning plays an essential role in a neurosurgical procedure of deep brain stimulation, leading to a successful procedure with significant surgical outcomes. Conventional preoperative path planning is time-consuming and uncertain, depending highly on the knowledge and experience of the clinician who has to manually plan the electrode-implant path. This work presents a new preoperative path planning strategy for neurostimulation to automatically and accurately find the optimal electrode-implant trajectory. Specifically, a coarse-to-refine neural network model is proposed to accurately segment anatomical brain structures such as the subthalamic nucleus, while the path planning is formulated as an optimization task that minimizes the surgical risk on the implantation trajectory through the segmented brain structures, as well as ensures the puncture path at the safest distance to targets of interest in the brain. We evaluate our method on retrospective neurostimulation data and compare it to the puncture path generated by experienced surgeons, with the experimental results showing that our method provides surgeons with automatic and accurate electrode implant trajectory comparable or even better than manual planning of fellow surgeons. Highlights: A coarse-to-refine deep learning segmentation method to extract the target and risk structures in 3D MRI data for the DBS planning. A safe and optimized puncture trajectory to mimic surgeons for image guided preoperative path planning with 3D MRI data. Evaluation of automatic puncture trajectory planning on postoperative 3D CT data.