Estimating the Joint Angles of a Magnetic Surgical Tool using Monocular 3D Keypoint Detection and Particle Filtering
Bibliographic record
Abstract
Magnetic surgical tools benefit greatly from real-time pose estimation, as this is essential for controlling them safely and effectively. Current pose estimation methods for surgical tools either focus on rigid tools, or are developed specifically for the da Vinci surgical system. In this work, we use computer vision from a monocular endoscopic camera to estimate the pose of an articulated magnetic surgical tool. In particular, we present a deep 3D keypoint estimation framework and a particle filter to achieve this. The former method can be used for any articulated surgical tool, while the latter method is specific to magnetic tools. We show that the deep 3D keypoint estimation framework estimates the surgical tool’s joint angles with an average error of 4.0 degrees and a speed of 29 Hz. In addition, we demonstrate the robustness of the magnetic particle filter and the deep pose estimation method for real-time tool pose estimation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".