Bibliographic record
Abstract
Image-guided surgery correlates pre-operative diagnostic patient images with the patient on the operating room table by using a patient-to-image registration and localizing and tracking the patient and the surgical instruments. The task of spatially aligning the patient with the diagnostic images into one view remains, however, with the surgeon. This task is not trivial, is time consuming, disrupts the workflow and may be prone to error. Augmented reality visualization has been proposed as a solution to traditional image-guided surgery systems. In augmented reality virtual objects are merged with the real world. In image-guided surgery augmented reality visualization is used to merge virtual patient models created from diagnostic images with the live view of the surgical scene. In the following dissertation the use of augmented reality visualization for image-guided neurovascular surgery is described. The dissertation begins by developing a taxonomy to describe augmented reality visualization in image-guided surgery and this taxonomy is used to describe the state of the art in the field. Next, the visualization of cerebral vascular data obtained through angiography is explored. Visualization of cerebral vasculature is important for the treatment of different vascular anomalies and malformations. Volume rendered 3D vascular images however, are difficult to comprehend spatially due to the many furcations in the vessels and the many vessels overlapping at different depths. By using perceptually driven volume rendering techniques the relative depth perception of these images can be improved. Different depth cue rendering techniques including chromadepth, fog, edges, kinetic depth, and stereopsis were explored in two novice and one expert study that looked at the effectiveness of the techniques in determining relative depth perception of vessels in cerebral angiograms.The results of these psychophysical experiments were brought into a clinical context by using them in our developed augmented reality image-guided surgery system. A typical problem with augmented reality visualization is that virtual objects tend to be perceived at the wrong depth. We explored the use of fog and edges, among other techniques, in the context of augmented reality visualization for image-guided neurovascular surgery to improve the depth perception of vessels in this context. The results of using our augmented reality image-guided surgery system in the operating room at the Montreal Neurological Hospital are presented for four different surgical cases. The results of this work show the promise of using augmented reality to improve surgical tasks and thereby improve patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".