3D visualization tool for minimally invasive discectomy assistance
Bibliographic record
Abstract
Multimodal fusion of 2D thoracoscopic images with a pre-operative 3D anatomical model of the spine is useful for minimally invasive surgical procedures using an angled monocular endoscope with varying focal length. An offline calibration procedure has been developed to compute initial endoscope parameters, such as lens distortion, focal length and optical center before surgery. An optical tracking system is used to update extrinsic parameters describing the position and orientation of the endoscope in real-time during the procedure. This calibration allows the registration of the thoracoscopic image sequence with a pre-operative MRI 3D model of the spine. Two visualization methods merging the 3D model and thoracoscopic image sequence have been developed using both augmented reality and augmented virtuality paradigms primarily as an aid for discectomy. Augmented views are generated by adding annotations and projecting the MRI 3D model onto real thoracoscopic images. Virtual views are generated by projecting the real thoracoscopic images on a virtual view of the 3D model. Experimental results showed that the calibration procedure accuracy obtained by computing the relative 3D reconstruction error on a known object was 1.0 mm. Two orthopedic surgeons assessed the generated views, confirming the relevance and added value of the proposed visualization tool for minimally invasive discectomy assistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".