MODELING TECHNIQUES FOR LIVER TISSUE PROPERTIES AND THEIR APPLICATION IN SURGICAL TREATMENT OF LIVER CANCER
Bibliographic record
Abstract
This chapter presents a modeling approach for soft tissue properties designed at Laval University as part of the development of a simulation system for liver surgery. Surgery simulation aims at providing physicians with tools allowing extensive training and precise planning of interventions. The design of such simulation systems requires accurate geometrical and mechanical models of the organs of the human body, as well as fast computation algorithms suitable for real-time conditions. Most existing systems use very simple mechanical models, based on the laws of linear elasticity. Numerous biomechanical results yet indicate that biological tissues exhibit much more complex behavior, including important non-linear and viscoelastic effects. In Sec. 1, we start by reviewing existing methods for the simulation of biological soft tissues. The approach used in our implementation, based on the tensor–mass model, is described in Sec. 2. In Sec. 3, we discuss the implementation issues and show how the efficiency of this model can be improved by an implementation on a distributed computer architecture. Finally, an experimental validation performed on liver tissue and an approach for simulating topological changes are presented in Secs. 4 and 5.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".