Haptic collision handling for simulation of transnasal surgery
Bibliographic record
Abstract
ABSTRACT Simulation of endoscopic navigation in the narrow nasal cavity poses important challenges to the computation of adequate and near‐realistic collision response and haptic feedback because extensive multidirectional contact and massive tissue deformations are inevitable. We present a virtual coupling algorithm that provides stable collision response as well as intuitive and smooth haptic interaction in all phases of the simulation. In each iteration, continuous collision detection between the point shell representing the surface of the virtual patient anatomy and the endoscope, represented by a cylinder, is performed. This allows for rolling back the instrument movement to the point in time the first collision occurred. Subsequently, a relaxation process locally optimizes the position and orientation of the instrument. A novel method of applying contact forces to colliding tissues and thus triggering appropriate deformations improves the fluency of navigation. This paper describes the algorithm and presents experimental results. © Her Majesty the Queen in Right of Canada 2012. Reproduced with the permission of the Minister of Medical Devices, National Research Council Canada.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".