Perceptual enhancement of arteriovenous malformation in MRI angiography displays
Bibliographic record
Abstract
The importance of presenting medical images in an intuitive and usable manner during a procedure is essential. However, most medical visualization interfaces, particularly those designed for minimally-invasive surgery, suffer from a number of issues as a consequence of disregarding the human perceptual, cognitive, and motor system's limitations. This matter is even more prominent when human visual system is overlooked during the design cycle. One example is the visualization of the neuro-vascular structures in MR angiography (MRA) images. This study investigates perceptual performance in the usability of a display to visualize blood vessels in MRA volumes using a contour enhancement technique. Our results show that when contours are enhanced, our participants, in general, can perform faster with higher level of accuracy when judging the connectivity of different vessels. One clinical outcome of such perceptual enhancement is improvement of spatial reasoning needed for planning complex neuro-vascular operations such as treating Arteriovenous Malformations (AVMs). The success of an AVM intervention greatly depends on fully understanding the anatomy of vascular structures. However, poor visualization of pre-operative MRA images makes the planning of such a treatment quite challenging.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".