Integrated volume visualization of functional image data and anatomical surfaces using normal fusion
Bibliographic record
Abstract
A generic method, called normal fusion, for integrated three-dimensional (3D) visualization of functional data with surfaces extracted from anatomical image data is described.The first part of the normal fusion method derives quantitative values from functional input data by sampling the latter along a path determined by the (inward) normal of a surface extracted from anatomical data; the functional information is thereby projected onto the anatomical surface independently of the viewpoint.Fusion of the anatomical and functional information is then performed with a color-encoding scheme based on the HSV model.This model is preferred over the RGB model to allow easy, rapid, and intuitive retrospective manipulation of the color encoding of the functional information in the integrated display, and two possible strategies for this manipulation are explained.The results first show several clinical examples that are used to demonstrate the viability of the normal fusion method.These same examples are then used to evaluate the two HSV color manipulation strategies.Furthermore, five nuclear medicine physicians used several other clinical cases to evaluate the overall approach for manipulation of the color encoded functional contribution to an integrated 3D visualization.The integrated display using the normal fusion technique combined with the added functionality provided by the retrospective color manipulation was highly appreciated by the clinicians and can be considered an important asset in the investigation of data from multiple modalities.Hum.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".