A mutual-information-based registration algorithm for ultrasound-guided computer-assisted orthopaedic surgery
Bibliographic record
Abstract
This paper presents a novel approach and its preliminary laboratory results for the employment of ultrasound (US) imaging in intraoperative guidance of computer-assisted orthopaedic surgeries (CAOS). The goal is to register live intraoperative US images with preoperative surgical planning data using minimal number of images. Preoperatively, a set of 2D US images are acquired with the corresponding positional information of the US probe provided by an optical tracking system. Using calibration parameters, the position of every pixel in the acquired images is transformed into the world coordinate frame to construct a 3D volumetric representation of the targeted anatomy for surgical planning. Intraoperatively, the surgeon takes live US images from the patient with the position of the US probe tracked in real time. A mutual-information-based registration algorithm is then used to find the closest match to the live image in the preoperative US image database. Because the position of the preoperative image inside the US volume is known, we are able to register the preoperative US volume to the live image, thus to the patient. Experiments have shown the registration algorithm has sub-millimeter accuracy in localizing the best match between the intraoperative and pre-operative images, demonstrating great potential for orthopaedic surgery applications. This method has some significant advantages over the previously reported US-guided CAOS techniques: it requires no segmentation, and employs only a few intraoperative images to accurately and robustly localize the patient. Preliminary laboratory results on both a Sawbones model of a radius bone and human subjects are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".