Group-wise registration of ultrasound to CT images of human vertebrae
Why this work is in the frame
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Bibliographic record
Abstract
Automatic registration of ultrasound (US) to computed tomography (CT) datasets is a challenge of considerable interest, particularly in orthopaedic and percutaneous interventions. We propose an algorithm for group-wise volume-to-volume registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed individually. The sub-volumes are then reconstructed into a single volume. The algorithm dynamically combines simulated US reflections from the vertebrae surfaces and surrounding soft tissue in the reconstructed CT, with scaled CT data to simulate US images of the spine anatomy. The simulated US data is used to register preoperative CT data to intra-operative US images. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. The registration is tested using a phantom of the lumbar spine (L3-L5). Initial misalignments of up to 8 mm were registered with a mean target registration error of 1.87±0.73 mm for L3, 2.79±0.93 mm for L4, 1.72±0.70 mm for L5, and 2.08±0.55 mm across the entire volume. To select an appropriate optimization strategy, we performed a volume-to- volume registration of US to CT of the lumbar spine, allowing no relative motion between vertebrae. We compare the results of this registration using three optimization strategies: simplex, gradient descent and CMA-ES. CMA-ES was found to converge slower than gradient descent and simplex, but was more robust for rigid volume-to-volume registration for initial misalignments up to 20 mm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it