Controlling virtual views in navigated breast conserving surgery
Bibliographic record
Abstract
PURPOSE: Lumpectomy is the resection of a tumor in the breast while retaining as much healthy tissue as possible. Navigated lumpectomy seeks to improve on the traditional technique by employing computer guidance to achieve the complete excision of the cancer with optimal retention of healthy tissue. Setting up navigation in the OR relies on the manual interactions of a trained technician to align three-dimensional virtual views to the patient’s physical position and maintain their alignment throughout surgery. This work develops automatic alignment tools to improve the operability of navigation software for lumpectomies. METHODS: Preset view buttons were developed to refine view setup to a single interaction. These buttons were tested by measuring the reduction in setup time and the number of manual interactions avoided through their use. An auto-center feature was created to ensure that three-dimensional models of anatomy and instruments were in the center of view throughout surgery. Recorded data from 32 lumpectomy cases were replayed and the number of auto-center view shifts was counted from the first cautery incision until the completion of the excision of cancerous tissue. RESULTS: View setup can now be performed in a single interaction compared to an average of 13 interactions (taking 83 seconds) when performed manually. The auto-center feature was activated an average of 33 times in the cases studied (n=32). CONCLUSION: The auto-center feature enhances the operability of the surgical navigation system, reducing the number of manual interactions required by a technician during the surgery. This feature along with preset camera view options are instrumental in the shift towards a completely surgeon-operable navigated lumpectomy system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".