Real-Time FEM-Based Registration of 3-D to 2.5-D Transrectal Ultrasound Images
Why this work is in the frame
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Bibliographic record
Abstract
We present a novel technique for real-time deformable registration of 3-D to 2.5-D transrectal ultrasound (TRUS) images for image-guided, robot-assisted laparoscopic radical prostatectomy (RALRP). For RALRP, a pre-operatively acquired 3-D TRUS image is registered to thin-volumes comprised of consecutive intra-operative 2-D TRUS images, where the optimal transformation is found using a gradient descent method based on analytical first and second order derivatives. Our method relies on an efficient algorithm for real-time extraction of arbitrary slices from a 3-D image deformed given a discrete mesh representation. We also propose and demonstrate an evaluation method that generates simulated models and images for RALRP by modeling tissue deformation through patient-specific finite-element models (FEM). We evaluated our method on in-vivo data from 11 patients collected during RALRP and focal therapy interventions. In the presence of an average landmark deformation of 3.89 and 4.62 mm, we achieved accuracies of 1.15 and 0.72 mm, respectively, on the synthetic and in-vivo data sets, with an average registration computation time of 264 ms, using MATLAB on a conventional PC. The results show that the real-time tracking of the prostate motion and deformation is feasible, enabling a real-time augmented reality-based guidance system for RALRP.].
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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 | 0.001 |
| 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) | 0.002 | 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