An Unsupervised Deep Learning Method for CT and MR Registration in Spine Surgery Simulator
Bibliographic record
Abstract
<p>Image registration is a fundamental step in patient specific spine surgery simulation where two images are aligned in the same spatial geometry coordinate space. Registration of Computed Tomography (CT) and Magnetic resonance imaging (MRI) has important implications for 3D model creation, clinical diagnosis, treatment planning, and image-guided surgery as it provides complimentary information obtained from different image modalities. Existing methods are slow and requires manual intervention. Therefore, a more accurate, robust and fast method was introduced with the implementation of a VoxelMorph framework using Modality Independent Neighbourhood Descriptor (MIND) based loss function. A UNET based model was trained with 268 pairs of CT and MRI images acquired through Sunnybrook Health Science Centre. The trained model was tested through 10 pairs of test data with vertebral body segmentation. The results achieved good performance accuracy; Dice Similarity Coefficient (DSC) : 0.845 ± 0.028 and Hausdorff distance : 1.128 ± 0.588 mm with regularization parameter (λ) tuned to 0.7. Integration of this method into a spine surgery simulation workflow will allow a fast (few seconds in GPU, under a minute in CPU), accurate and robust registration process.</p>
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 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".