Modelling Prostate Deformation: SOFA versus Experiments
Bibliographic record
Abstract
Needle insertion procedures are commonly used to treat and to diagnose prostate cancer. Surgical simulation systems can be used to estimate prostate deformation during pre- and intra-operative needle insertion planning. Such systems require a model that can accurately predict the prostate deformation in real time. In this study, we present a prostate model that incorporates the anatomy of the male pelvic region. The model is used to predict the prostate deformation during needle insertion and it is implemented in the Simulation Open Framework Architecture (SOFA). SOFA simulations are compared with experimental results for two scenarios: indentation and needle insertion. An experimental phantom is developed using anatomically accurate magnetic resonance images and populated with elasticity properties obtained from ultrasound-based Acoustic Radiation Force Impulse imaging technique. Markers are placed on the phantom surface to identify the deformation during indentation experiments. The root mean square error (RMSE) obtained in indentation experiments is 0.36 mm. During the needle insertion, the needle tip position is used to validate the model. The SOFA simulation resulted in a RMSE of 0.14 mm. The results of this study demonstrate that SOFA is a feasible option to be used in surgical simulations for pre-operative planning and training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".