P.116 Anatomical fiducials used to quantify localization and registration accuracy in deep brain stimulation
Bibliographic record
Abstract
Background: Studies of deep brain stimulation (DBS) require accurate electrode localization and image registration. We used anatomical fiducials to investigate localization and registration errors in patients who underwent subthalamic nucleus (STN) DBS for Parkinson’s disease (PD). Methods: We conducted a retrospective analysis of patients who underwent bilateral STN DBS for PD. Pre and post operative MRI scans were non-linearly normalized to a standard template (MNI152NLin2009bAsym). Four raters localized DBS electrodes (Lead-DBS), the anterior commissure (AC) and posterior commissure (PC). Errors between rater localizations were calculated (fiducial localization error; FLE). We transformed AC and PC coordinates from template to patient space to calculate the fiducial registration error (FRE)Results: Ninety-nine patients were analyzed, with a median FLE of 0.76mm, 0.74mm, 0.71mm and 0.66mm for the right electrode, left electrode, AC and PC respectively (no significant difference, Wilcoxon sign rank). The median FRE was 1.59mm for AC and 1.21mm for PC, significantly higher than FLE at those coordinates (Wilcoxon sign rank, p<0.001). Conclusions: Raters can accurately localize DBS electrodes, AC and PC from clinical images with sub-millimetric accuracy. Higher FREs at AC and PC suggested registration errors may contribute more than localization errors to electrode uncertainty in a common space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".