Direct validation of atlas‐based red nucleus identification for functional radiosurgery
Bibliographic record
Abstract
Treatment targets in functional neurosurgery usually consist of selected structures within the thalamus and basal ganglia, which can be stimulated in order to affect specific brain pathways. Chronic electrical stimulation of these structures is a widely used approach for selected patients with advanced movement disorders. An alternative therapeutic solution consists of producing a lesion in the target nucleus, for example by means of radiosurgery, a noninvasive procedure, and this prevents the use of intraoperative microelectrode recording as a method for accurate target definition. The need to have accurate noninvasive localization of the target motivated our previous work on atlas-based identification; the aim of this present work is to provide additional validation of this approach based on the identification of the red nuclei (RN), which are located near the subthalamic nucleus (STN). Coordinates of RN were obtained from the Talairach and Tournoux (TT) atlas and transformed into the coordinates of the Montreal Neurological Institute (MNI) atlas, creating a mask representation of RN. The MNI atlas volume was nonrigidly registered onto the patient magnetic resonance imaging (MRI). This deformation field was then applied to the RN mask, providing its location on the patient MRI. Because RN are easily identifiable on 1.5 T T2-MRI images, they were manually delineated; the coordinates of the centers of mass of the manually and automatically identified structures were compared. Additionally, volumetric overlapping indices were calculated. Ten patients were examined by this technique. All indices indicated a high level of agreement between manually and automatically identified structures. These results not only confirm the accuracy of the method but also allow fine tuning of the automatic identification method to be performed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".