Toward Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery
Bibliographic record
Abstract
Image-guided neurosurgery allows surgeons to view their tools in relation to preoperatively acquired patient images and models. To continue using neuronavigation systems throughout operations, image registration between preoperative images [typically magnetic resonance imaging (MRI)] and intraoperative images (e.g., ultrasound) is common to account for brain shift (deformations of the brain during surgery). We implemented a method to estimate MRI-ultrasound registration errors, with the goal of enabling surgeons to quantitatively assess the performance of linear or nonlinear registrations. To the best of our knowledge, this is the first dense error estimating algorithm applied to multimodal image registrations. The algorithm is based on a previously proposed sliding-window convolutional neural network that operates on a voxelwise basis. To create training data where the true registration error is known, simulated ultrasound images were created from preoperative MRI images and artificially deformed. The model was evaluated on artificially deformed simulated ultrasound data and real ultrasound data with manually annotated landmark points. The model achieved a mean absolute error (MAE) of 0.977 ± 0.988 mm and a correlation of 0.8 ± 0.062 on the simulated ultrasound data, and an MAE of 2.24 ± 1.89 mm and a correlation of 0.246 on the real ultrasound data. We discuss concrete areas to improve the results on real ultrasound data. Our progress lays the foundation for future developments and ultimately implementation of clinical neuronavigation systems.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".