Automatic Segmentation of the Subthalamic Nucleus: A Viable Option to Support Planning and Visualization of Patient-Specific Targeting in Deep Brain Stimulation. Issue 5 (12th March 2019)

• Record Type: Journal Article
• Authors: Reinacher, Peter C; Várkuti, Bálint; Krüger, Marie T; Piroth, Tobias; Egger, Karl; Roelz, Roland; Coenen, Volker A
• Is Part Of: Operative neurosurgery. Volume 17:Issue 5(2019)
• Page Start: 497
• Page End: 502
• Publication Date: 2019-03-12
• Subjects: Automatic anatomical segmentation -- Deep brain stimulation -- Intraoperative microelectrode recording -- Subthalamic nucleus; Nervous system -- Surgery -- Periodicals; 617.480590
• DOI: 10.1093/ons/opz015
• Languages: English
• ISSNs: 2332-4252

Abstract

Abstract: BACKGROUND: Automatic segmentation is gaining relevancy in image-based targeting of neural structures. OBJECTIVE: To evaluate its feasibility, we retrospectively analyzed the concordance of magnetic resonance imaging (MRI)-based automatic segmentation of the subthalamic nucleus (STN) and intraoperative microelectrode recordings (MERs). METHODS: Electrodes (n = 60) for deep brain stimulation were implanted in the STN of patients (n = 30; median age 57 yr) with Parkinson disease (n = 29) or rapid-onset dystonia parkinsonism (n = 1). Elements (Brainlab, Munich, Germany) were used to segment the STN, using 2 volumetric T1 (±contrast) and volumetric T2 images as input. The stereotactic computed tomography was coregistered with the imaging, and the original stereotactic coordinates were imported. MERs (0.5-1 mm steps) along the anterior, central, and lateral trajectories were used to determine differences between the image-segmented STN boundary and MER-based STN entry and exit. RESULTS: Of 175 trajectories, 105 penetrated or touched (≤0.7 mm) the STN. The overall median deviation between the segmented STN boundary and electrophysiological recordings was 1.1 mm for MER-based STN entry and 2.0 mm for STN exit. Regarding the entry point of the STN, there was no statistically significant difference between MRI-based automatic segmentation and the electrophysiological trajectories analyzed with intraoperative MER. The exit point was significantly different between both methods in the central and lateral trajectories. CONCLUSION: MRI-based automatic segmentation of the STN is a viable, patient-specific targeting approach that can be used alongside traditional targeting methods in deep brain stimulation to support preoperative planning and visualization of target structures and aid postoperative optimization of programming.