Fast Contextual View Generation and Region of Interest Selection in 3D Medical Images Via Superellipsoid Manipulation, Blending and Constrained Region Growing
Bibliographic record
Abstract
This thesis presents a 3D widget user-interface (UI), super-ellipsoid shape primitives and a customized volume rendering algorithm that together create a system effective for exploring 3D medical images and for selecting a 3D region within these images. Using a “painting” metaphor, the widget UI supports the fast and precise positioning of a super-ellipsoid shaped paint “blob”. The paint blob can be “deposited” and automatically blended with previously deposited blobs to form arbitrarily-shaped regions enclosing target image features. The rendering of these “focus” regions can be controlled separately from the surrounding contextual region, allowing medical experts to examine and measure image features relative to the context. The system’s core algorithms are designed to execute on Graphics Processing Units (GPUs), resulting in real-time interaction and high-quality visualizations. The focus plus context visualization system presented in this thesis is validated via a user study and a series of experiments.
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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) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".