Registration of 3D Ultrasound Through an Air–Tissue Boundary
Bibliographic record
Abstract
In this study we evaluated a new method for registering three-dimensional ultrasound (3DUS) data to external coordinate systems. First, 3DUS was registered to the stereo endoscope of a da Vinci Surgical System by placing a registration tool against an air-tissue boundary so that the 3DUS could image ultrasound fiducials while the stereo endoscope could image camera markers on the same tool. The common points were used to solve the registration between the 3DUS and camera coordinate systems. The target registration error (TRE) when imaging through a PVC tissue phantom ranged from 3.85 1.76 mm to 1.82 1.03 mm using one to four registration tool positions. TRE when imaging through an ex-vivo liver tissue sample ranged from 2.36 1.01 mm to 1.51 0.70 mm using one to four registration tool positions. Second, using a similar method, 3DUS was registered to the kinematic coordinate system of a da Vinci Surgical System by using the da Vinci surgical manipulators to identify common points on an air-tissue boundary. TRE when imaging through a PVC tissue phantom was 0.95 0.38 mm. This registration method is simpler and potentially more accurate than methods using commercial motion tracking systems. This method may be useful in the future in augmented reality systems for laparoscopic and robotic-assisted surgery.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".