The VascuLens: A Handsfree Projector-Based Augmented Reality System for Surgical Guidance During DIEP Flap Harvest
Bibliographic record
Abstract
Augmented reality technologies are increasinglybeing used to provide enhanced surgical navigation forsurgeons. The goal of such augmented reality technology is toimprove both the safety and efficiency of operations. TheVascuLens, a novel handsfree and focus free projector-basedaugmented reality system, is presented in this paper. Theproposed application for the VascuLens is for improvingvisualization of the vascular anatomy during deep inferiorepigastric perforator (DIEP) flap breast reconstruction. TheDIEP flap is a fasciocutaneous flap that is harvested based onperforating vessels 1-2mm in size and then connected underthe microscope to the internal mammary vessels in the chest tocreate a new breast mound after mastectomy. The VascuLenssystem aims to take preoperative CT scan data, register thepreoperative data to the patient on the operating room table,and project the segmented DIEP arteries directly onto thepatient. The novel aspects of the system include: 1) a handsfreeprojector, 2) a simple preoperative to intraoperative imageregistration technique that does not require a fiducial markeror camera, 3) and intraoperative surgeon-in-the-loop surgicalguidance. This paper describes the proof-of-concept Vasculensworkflow and reports the Vasculens accuracy. The accuracy isreported as a function of registration technique, patient bodytype, projector height and projector angle. Using the idealregistration technique, projector height and projector angle,the mean absolute point reprojection error is 1.7mm, making ita good candidate for DIEP flap breast reconstruction surgery.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".