Exploration of fused multi-volume images using user-defined binary masks
Bibliographic record
Abstract
Acquisition and fusion of multiple imaging modalities is becoming an increasingly desired clinical practice. This is particularly the case in radiation therapy, where dosage must be determined using electron density calculations from CT images, which lack the contrast to resolve soft-tissue structures. This often necessitates the fusion of corresponding MRI images in order to plot radiation trajectories safely around critical tissues. Simultaneous visualization of multiple volumes using direct volume rendering (DVR) techniques offers a number of advantages over traditional visualization methods. Specifically, fused visualization enhances the relational aspects of volumes, providing improved context [cite the first one]. However, there are many challenges involved in implementing DVR using fused data sets. The primary challenge is determining how images overlap to provide meaningful information. Additionally, there is increased computational complexity beyond standard DVR techniques, threatening real-time applications of fused DVR. These difficulties are evident to users of such systems as they must manage complex user interfaces and poor application performance. In this work, we introduce a user-centric multi-volume DVR technique which addresses issues of performance and ease of use. Through an intuitive interface, users are able to spatially define regions of interest, determining the relative contributions of each modality in the output rendering.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".