SEEGAtlas: A framework for the identification and classification of depth electrodes using clinical images
Bibliographic record
Abstract
Abstract Objective. Accurate localization, classification, and visualization of intracranial electrodes are fundamental for analyzing intracranial electrographic recordings. While manual contact localization is the most common approach, it is time-consuming, prone to errors, and is particularly challenging and subjective in low quality images, which are common in clinical practice. Automatically locating and interactively visualizing where each of the 100–200 individual contacts records in the brain is essential for understanding the neural origins of intracranial EEG. Approach. We introduced the SEEGAtlas plugin for the IBIS system, an open-source software platform for image-guided neurosurgery and multi-modal image visualization. SEEGAtlas extends IBIS functionalities to semi-automatically locate depth-electrode contact coordinates and automatically label the tissue type and anatomical region in which each contact is located. To illustrate the capabilities of SEEGAtlas and to validate the algorithms, clinical magnetic resonance images (MRIs) before and after electrode implantation of ten patients with depth electrodes implanted to localize the origin of their epileptic seizures were analyzed. Main Results . Visually identified contact coordinates were compared with the coordinates obtained by SEEGAtlas, resulting in a median difference of 1.4 mm. The agreement was lower for MRIs with weak susceptibility artifacts than for high-quality images. The tissue type was classified with 86% agreement with visual inspection. The anatomical region was classified as having a median agreement across patients of 82%. Significance . The SEEGAtlas plugin is user-friendly and enables accurate localization and anatomical labeling of individual contacts along implanted electrodes, together with powerful visualization tools. Employing the open-source SEEGAtlas results in accurate analysis of the recorded intracranial electroencephalography (EEG), even when only suboptimal clinical imaging is available. A better understanding of the cortical origin of intracranial EEG would help improve clinical interpretation and answer fundamental questions of human neuroscience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".