Model based stabilization of soft tissue targets in needle insertion procedures
Bibliographic record
Abstract
This paper presents a soft tissue target stabilization method during needle insertion procedures. The object considered in this study may have fixed boundary sections and limited surface exposed to external manipulation. The target must be stabilized along the needle path during the needle insertion. It is assumed that a paddle with fixed geometry is available for deformable object manipulation. Two approaches were considered for the target stabilization problem. The first approach uses a static paddle placed on the available boundary, at a strategic location, such that the target motion orthogonal to the needle axis is minimized during the needle insertion. The second approach uses a dynamic paddle attached to the available boundary for the active compensation of the target deflection. In this paper we analyze the optimal paddle placement for the two proposed approaches and present initial numerical results for the case of homogeneous and nonhomogeneous deformable objects. The results show that the first approach is sensitive to possible non-homogeneities in the object, therefore it is not robust to modeling errors. The results also show that optimal placement for the second approach is less sensitive to modeling errors, making it more desirable for physical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".