ViC : Virtual Cadaver - A Prototype Extendible System for Building Photorealistic Interactive Visualizations of Human Anatomy Using Game Development Technology
Bibliographic record
Abstract
This thesis presents a Virtual Cadaver system, ViC, which allows users to interactively “cut open” and visualize a highly realistic representation of 3D human anatomy, including skin, muscles, bones, arteries and many other anatomical systems and structures. ViC’s interaction and visualization functionality, coupled with a multi-touch interface, provide real-time cutaway operations using simple and familiar gestures. To support the cutaway feature, ViC’s 3D human anatomy dataset is preprocessed, breaking up the large anatomy system data meshes into smaller, more manageable mesh “fragments”. The shape of each fragment is crafted to support semantically meaningful cutaways while the “granularity” of the fragments supports interaction efficiency. ViC uses game technology to enable highly realistic rendering of human anatomy. Furthermore, ViC’s performance and cutaway capabilities were evaluated on consumer-grade hardware to confirm that real-time interaction and visualization with highly-responsive multi-touch input actions can be achieved.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".