The control unit for a head mounted operating microscope used for augmented reality visualization in computer aided surgery
Bibliographic record
Abstract
Two main concepts of head mounted displays (HMD) for augmented reality (AR) visualization exist, the optical and video-see through type. Several research groups have pursued both approaches for utilizing HMDs for computer aided surgery. While the hardware requirements for a video see through HMD to achieve acceptable time delay and frame rate seem to be enormous the clinical acceptance of such a device is doubtful from a practical point of view. Starting from previous work in displaying additional computer-generated graphics in operating microscopes, we have adapted a miniature head mounted operating microscope for AR by integrating two very small computer displays. To calibrate the projection parameters of this so called varioscope AR we have used Tsai's (1987) algorithm for camera calibration. Connection to a surgical navigation system was performed by defining an open interface to the control unit of the varioscope AR. The control unit consists of a standard PC with an dual head graphics adapter to render and display the desired augmentation of the scene. We connected this control unit to an computer aided surgery (CAS) system by the TCP/IP interface. In this paper we present the control unit for the HMD and its software design. We tested two different optical tracking systems, the Flash-point (Image Guided Technologies, Boulder, CO), which provided about 10 frames per second, and the Polaris (Northern Digital, Ontario, Can) which provided at least 30 frames per second, both with a time delay of one frame.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".