Applications of VR medical image visualization to chordal length measurements for cardiac procedures
Bibliographic record
Abstract
Cardiac surgeons rely on diagnostic imaging for preoperative planning. Recently, developments have been made on improving 3D ultrasound (US) spatial compounding tailored for cardiac images. Compounded 3D ultrasound volumes are able to capture complex anatomical structures at a level similar to a CT scan, however these images are difficult to display and visualize due to an increased amount of surrounding tissue captured including excess noise at the volume boundaries. Traditional medical image visualization software does not easily allow for viewing 2D slices at arbitrary angles, and 3D rendering techniques do not adequately capture depth information without the use of advanced transfer functions or other depth-encoding techniques that must be tuned to each individual data set. Previous studies have shown that the effective use of virtual reality (VR) can improve image visualization, usability and reduce surgical errors in case planning. We demonstrate the novel use of a VR system for the application of measuring chordal lengths from compounded transesophageal and transgastric echocardiography (TEE, TTE) ultrasound images. Compounded images are constructed from TEE (en-face) views registered and spatially compounded with multiple TEE transgastric views in order to capture both the mitral valve leaflets and chordae tendineae with high levels of detail. Users performed the task of taking linear measurements of chordae visible in these images using both traditional software and a VR platform. Compared to traditional software, the VR platform offered a more intuitive experience with respect to orientation, however users felt there was a lack of precision when performing the measurement tasks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".