Stereoscopic display technologies, interaction paradigms, and rendering approaches for neurosurgical visualization
Bibliographic record
Abstract
We conducted a comparative study of different stereoscopic display modalities (head-mounted display, polarized projection, and multiview lenticular display) to evaluate their efficacy in supporting manipulation and understanding of 3D content, specifically, in the context of neurosurgical visualization. Our study was intended to quantify the differences in resulting task performance between these choices of display technology. The experimental configuration involved a segmented brain vasculature and a simulated tumor. Subjects were asked to manipulate the vasculature and a pen-like virtual probe in order to define a vessel-free path from cortical surface to the targeted tumor. Because of the anatomical complexity, defining such a path can be a challenging task. To evaluate the system, we quantified performance differences under three different stereoscopic viewing conditions. Our results indicate that, on average, participants achieved best performance using polarized projection, and worst with the multiview lenticular display. These quantitative measurements were further reinforced by the subjects' responses to our post-test questionnaire regarding personal preferences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".