Needle Tracking and Deflection Prediction for Robot-Assisted Needle Insertion Using 2D Ultrasound Images
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Bibliographic record
Abstract
In many types of percutaneous needle insertion surgeries, tissue deformation and needle deflection can create significant difficulties for accurate needle placement. In this paper, we present a method for automatic needle tracking in 2D ultrasound (US) images, which is used in a needle–tissue interaction model to estimate current and future needle tip deflection. This is demonstrated using a semi-automatic needle steering system. The US probe can be controlled to follow the needle tip or it can be stopped at an appropriate position to avoid tissue deformation of the target area. US images are used to fully parameterize the needle-tissue model. Once the needle deflection reaches a pre-determined threshold, the robot rotates the needle to correct the tip’s trajectory. Experimental results show that the final needle tip deflection can be estimated with average accuracies between 0.7[Formula: see text]mm and 1.0[Formula: see text]mm for insertions with and without rotation. The proposed method provides surgeons with improved US feedback of the needle tip deflection and minimizes the motion of the US probe to reduce tissue deformation of the target area.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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