Deformable registration for intra-operative cone-beam CT guidance of head and neck surgery
Bibliographic record
Abstract
The computational and geometric performance of Demons deformable registration techniques were investigated in the application of cone-beam CT (CBCT) for intra-operative guidance of head and neck surgery. A prototype C-arm providing CBCT images of sub-mm spatial resolution and soft-tissue contrast was used to acquire images of a cadaveric head before and after surgical intervention. Four deformable registration methods were investigated both in terms of their convergence behavior (time and number of iterations required) and registration accuracy (correlation between deformed and target images and the corresponding target registration error, TRE). Rigid registration alone exhibited a TRE of (2.6 +/- 1.0) mm, compared to a TRE of (0.8 +/- 0.3) mm obtained with deformable registration. A fast symmetric demons implementation was identified as the most suitable for intra-operative use in terms of speed, image quality, and accuracy. Increasing the spatial resolution of CBCT images was found to increase registration accuracy at the cost of computational expense. Intra-operative CBCT combined with deformable registration offers to overcome conventional limitations of guidance by preoperative images alone and presents an accurate method of integrating imaging and planning data in a manner that properly reflects changes in the intra-operative state.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".