Tactics: an open-source platform for planning, simulating and validating stereotactic surgery
Bibliographic record
Abstract
Frame-based stereotaxy is widely used for planning and implanting deep-brain electrodes. In 2013, as part of a clinical study on deep-brain stimulation for treatment-resistant depression, our group identified a need for software to simulate and plan stereotactic procedures. Shortcomings in extant commercial systems encouraged us to develop Tactics. Tactics is purpose-designed for frame-based stereotactic placement of electrodes. The workflow is far simpler than commercial systems. By simulating specific electrode placement, immediate in-context view of each electrode contact, and the cortical entry site are available within seconds. Post implantation, electrode placement is verified by linearly registering post-operative images. Tactics has been particularly helpful for invasive electroencephalography electrodes where as many as 20 electrodes are planned and placed within minutes. Currently, no commercial system has a workflow supporting the efficient placement of this many electrodes. Tactics includes a novel implementation of automated frame localization and a user-extensible mechanism for importing electrode specifications for visualization of individual electrode contacts. The system was systematically validated, through comparison against gold-standard techniques and quantitative analysis of targeting accuracy using a purpose-built imaging phantom mountable by a stereotactic frame. Internal to our research group, Tactics has been used to plan over 300 depth-electrode targets and trajectories in over 50 surgical cases, and to plan dozens of stereotactic biopsies. Source code and pre-built binaries for Tactics are public and open-source, enabling use and contribution by the extended community.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.033 |
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".