Needle insertion into soft tissue: A survey
Bibliographic record
Abstract
Needle insertion in soft tissue has attracted considerable attention in recent years due to its application in minimally invasive percutaneous procedures such as biopsies and brachytherapy. This paper presents a survey of the current state of research on needle insertion in soft tissue. It examines the topic from several aspects, e.g. modeling needle insertion forces, modeling tissue deformation and needle deflection during insertion, robot-assisted needle insertion, and the effect of different trajectories on tissue deformation. All studies show that the axial force of a needle during insertion in soft tissue is the summation of different forces distributed along the needle shaft such as stiffness force, frictional force and cutting force. Some studies have modeled these forces. The force data in some procedures is used for identifying tissue layers as the needle is inserted or for path planning. Needle deflection and tissue deformation are major problems for accurate needle insertion and attempts have been made to model them. Using current models several insertion techniques have been developed which are briefly reviewed in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".