Efficient automatic 2D/3D registration of cardiac ultrasound and CT images
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Bibliographic record
Abstract
Hybrid ablations are a promising but difficult intervention for the treatment of atrial fibrillation. With the ultimate goal of providing navigation support for such procedures, we investigate a registration algorithm for routinely available preoperative and intraoperative images. We propose a fully automatic segmentation algorithm for the boundaries of cardiac chambers in intraoperative TEE ultrasound using a generic heart model. The resulting ultrasound segmentations are initially registered to the preoperative CT model using a frame-to-slice search, which is then refined using an efficient continuous optimisation. Results are presented for data sets from three patients who underwent hybrid ablations at our institution. The mean time to process a single ultrasound image from segmentation to registration with CT was 1.5 s, with the mean RMS error across the sequence being 4.8 mm. With further validation, these results show promise for surgical navigation in hybrid ablation procedures.
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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 | 0.000 | 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 | 0.000 |
| 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