Single camera closed-form real-time needle trajectory tracking for ultrasound
Bibliographic record
Abstract
In ultrasound-guided needle insertion procedures, tracking of the needle relative to the ultrasound image is beneficial for needle trajectory planning and guidance. A single camera closed-form method is proposed for automatic real-time trajectory tracking with a low-cost camera mounted directly on the ultrasound transducer. The camera is calibrated to the ultrasound image coordinates. By mounting the camera on the transducer, issues of visual obstruction are reduced and accuracy of tracking is increased compared to camera-tracking systems with a fixed case. Compared to previous work with stereo cameras, a single camera further reduces cost, complexity and size, but requires a needle with known markings. The proposed solution uses the depth markings etched on many common needles (e.g. epidural needle). A fully automatic image processing method has been developed for real-time identification of the needle trajectory using a novel closed-form solution based on three identified markings and the camera's intrinsic calibration parameters. The trajectory of the needle relative to the ultrasound image is calculated and displayed. Validation compares the calculated intersection of the needle trajectory to the ultrasound image with the depiction of the actual needle intersection in the image. The overall error is 3.0 ± 2.6 mm for a low-cost 640×480 pixel USB camera.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 0.002 |
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".