Robust, Intrinsic Tracking of a Laparoscopic Ultrasound Probe for Ultrasound-Augmented Laparoscopy
Bibliographic record
Abstract
In situ visualization of laparoscopic ultrasound in both conventional and robot-assisted laparoscopic surgery requires robust and efficient computation of the pose of the laparoscopic ultrasound probe with respect to the laparoscopic camera. Image-based intrinsic methods of computing this relative pose need to overcome challenges due to irregular illumination, partial feature occlusion, and clutter that are unavoidable in practical laparoscopic surgery. In this paper, we propose an accurate image-based method that is robust to partial occlusion of the fiducials and outliers. The method is extended to multi-view imaging model with applications in stereoscopic laparoscopy and robot-assisted surgery. Rather than treating the model-to-image correspondence and pose computation as separate problems, we solve them jointly using the Kalman Filter-based framework that demonstrates video rate running time (~24fps). By keeping the optical tracking measurements as a reference, we demonstrate that the proposed methods result in clinically acceptable tracking accuracy, reaching target registration errors well below 1.5mm on average. In addition, our multi-view tracking method is compared to a conventional stereo triangulation-based pose estimation scheme that commercial optical tracking systems are based on, to experimentally demonstrate its superiority in terms of accuracy. Finally, we qualitatively demonstrate the suitability of our methods for practical laparoscopic applications by conducting a phantom-based experiment.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".