Structured-light three-dimensional surface scanning with deep learning-assisted N-bar localizer detection for stereotactic registration: a technical feasibility study in deep brain stimulation
Abstract
Intraoperative computed tomography (CT) is the clinical standard for stereotactic registration in deep brain stimulation (DBS) surgery, but it adds ionizing radiation, operative time, and workflow complexity. Whether an optical surface-scanning approach could ultimately support CT-independent stereotactic registration remains unknown. In this technical feasibility study, we developed a structured-light three-dimensional surface scanning (3DSS) workflow with deep learning (DL)-assisted N-bar localizer detection and quantified its agreement with conventional CT-based registration. Ten patients undergoing frame-based DBS for Parkinson’s disease or essential tremor underwent handheld craniofacial and N-bar scanning, completed in 1–3 min without interrupting surgery. CT-derived craniofacial surface models were co-registered with the surface scans using coarse-to-fine point-cloud registration with iterative closest point (ICP) refinement. A supervised DL object-detection model was used specifically to identify N-bar rod cross-sections from voxelized surface scans; and the detected localizer geometry was registered to the known N-bar model using deterministic point-to-line ICP to derive the 3DSS-based stereotactic transformation. Across the 10 independent patient registrations, CT–3DSS surface alignment reached a mean absolute error of 0.85 ± 0.10 mm, and the resulting 20 hemisphere-level comparisons showed a mean CT–3DSS target-coordinate deviation of 1.91 ± 0.87 mm and a mean trajectory angular deviation of 0.81° ± 0.60°. Despite these average levels of agreement, 10 of 20 target deviations exceeded 2 mm (maximum, 3.48 mm), underscoring the variability of the current approach. In addition, the present implementation remains CT-dependent because a CT-derived craniofacial surface is required to map the 3DSS data into the CT/DICOM world coordinate system, while CT-based stereotactic registration served as the comparison reference. Taken together, these preliminary single-center findings demonstrate technical feasibility while quantifying the performance gap that must be addressed through hardware and algorithmic refinements before radiation-free, CT-independent stereotactic registration can be considered for clinical use.
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BibTeX
@article{koller2026structuredlight,
title = {{Structured-light three-dimensional surface scanning with deep learning-assisted N-bar localizer detection for stereotactic registration: a technical feasibility study in deep brain stimulation}},
author = {Adam L. Koller and Hojin Shin and Jennifer Tang-Cabrera and Adriano Alfonsi and Elliott Lee and Paul J. Chen and Maximilian A. Hawkes and Yoonbae Oh and Kendall H. Lee and Jaeyun Sung},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-73260-w},
url = {https://www.nature.com/articles/s41598-026-73260-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Structured-light three-dimensional surface scanning with deep learning-assisted N-bar localizer detection for stereotactic registration: a technical feasibility study in deep brain stimulation
AU - Adam L. Koller
AU - Hojin Shin
AU - Jennifer Tang-Cabrera
AU - Adriano Alfonsi
AU - Elliott Lee
AU - Paul J. Chen
AU - Maximilian A. Hawkes
AU - Yoonbae Oh
AU - Kendall H. Lee
AU - Jaeyun Sung
PY - 2026
DA - 2026/09/30
JO - Scientific Reports
DO - 10.1038/s41598-026-73260-w
UR - https://www.nature.com/articles/s41598-026-73260-w
AB - Intraoperative computed tomography (CT) is the clinical standard for stereotactic registration in deep brain stimulation (DBS) surgery, but it adds ionizing radiation, operative time, and workflow complexity. Whether an optical surface-scanning approach could ultimately support CT-independent stereotactic registration remains unknown. In this technical feasibility study, we developed a structured-light three-dimensional surface scanning (3DSS) workflow with deep learning (DL)-assisted N-bar localizer detection and quantified its agreement with conventional CT-based registration. Ten patients undergoing frame-based DBS for Parkinson’s disease or essential tremor underwent handheld craniofacial and N-bar scanning, completed in 1–3 min without interrupting surgery. CT-derived craniofacial surface models were co-registered with the surface scans using coarse-to-fine point-cloud registration with iterative closest point (ICP) refinement. A supervised DL object-detection model was used specifically to identify N-bar rod cross-sections from voxelized surface scans; and the detected localizer geometry was registered to the known N-bar model using deterministic point-to-line ICP to derive the 3DSS-based stereotactic transformation. Across the 10 independent patient registrations, CT–3DSS surface alignment reached a mean absolute error of 0.85 ± 0.10 mm, and the resulting 20 hemisphere-level comparisons showed a mean CT–3DSS target-coordinate deviation of 1.91 ± 0.87 mm and a mean trajectory angular deviation of 0.81° ± 0.60°. Despite these average levels of agreement, 10 of 20 target deviations exceeded 2 mm (maximum, 3.48 mm), underscoring the variability of the current approach. In addition, the present implementation remains CT-dependent because a CT-derived craniofacial surface is required to map the 3DSS data into the CT/DICOM world coordinate system, while CT-based stereotactic registration served as the comparison reference. Taken together, these preliminary single-center findings demonstrate technical feasibility while quantifying the performance gap that must be addressed through hardware and algorithmic refinements before radiation-free, CT-independent stereotactic registration can be considered for clinical use.
SN - 2045-2322
ER -