Integration and accuracy evaluation of exoscope-based stereovision for image updating in neurosurgery
Bibliographic record
Abstract
In image-guided neurosurgery, intraoperative navigation typically relies on pre-operative magnetic resonance (pMR) images. However, after dural opening, the accuracy of image guidance degrades due to factors such as gravity, loss of cerebrospinal fluid, intracranial pressure changes, and medication. Consequently, the brain can deform up to 1 cm, rendering pMR image guidance beyond the acceptable range. We developed an image-updating approach to account for intraoperative brain deformation by deforming pMR to match the surgical scene, achieving a target registration error (TRE) of 1.60 ± 0.43 mm. The image updating system uses a navigation system for tracking and an intraoperative stereovision system (iSV) mounted to a surgical microscope for obtaining the intraoperative surface profile of the exposed brain. Recently, exoscopes, such as Synaptive Modus X, which integrates navigation and 3D visualization, have become popular in neurosurgery as they allow multiple users to have depth perception simultaneously. We aim to integrate stereovision data from an exoscope system into the existing image updating pipeline. Although the exoscope provides depth perception, quantitative reconstruction and localization of the surgical scene are not currently available. In this study, we adapted our iSV acquisition and reconstruction for exoscope systems. Navigation information was acquired via OpenIGTLink, and image data via frame grabbers. Stereo parameters and the spatial relationship between cameras and tracker were calibrated using a tracked checkerboard. The accuracy of stereo calibration and reconstruction was compared to our previous microscope-based implementation. Results showed that the exoscope-based and microscope-based iSV systems were comparable. The calibration error for the exoscope was 0.6±0.2 mm, while the microscope exhibited an error of 0.6±0.1 mm. The iSV reconstruction errors were 1.4±0.5 mm and 1.4±1.4 mm for the exoscope and microscope, respectively, suggesting that the exoscope-based iSV system can provide intraoperative data for brain deformation compensation.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".