Automatic Registration and Error Color Maps to Improve Accuracy for Navigated Bone Tumor Surgery Using Intraoperative Cone-Beam CT
Bibliographic record
Abstract
Computer-assisted surgery (CAS) can improve surgical precision in orthopaedic oncology. Accurate alignment of the patient's imaging coordinates with the anatomy, known as registration, is one of the most challenging aspects of CAS and can be associated with substantial error. Using intraoperative, on-the-table, cone-beam computed tomography (CBCT), we performed a pilot clinical study to validate a method for automatic intraoperative registration. Methods: Patients who were ≥18 years of age, had benign bone tumors, and underwent resection were prospectively enrolled. In addition to inserting a navigation tracking tool into the exposed bone adjacent to the surgical field, 2 custom plastic ULTEM tracking tools (UTTs) were attached to each patient's skin adjacent to the tumor using an adhesive. These were automatically localized within the 3-dimensional CBCT volume to be used as image landmarks for registration, and the corresponding tracker landmarks were captured using an infrared camera. The main outcomes were the fiducial registration error (FRE) and the target registration error (TRE). The navigation time was recorded. Results: Thirteen patients with benign tumors in the femur (n = 10), tibia (n = 2), and humerus (n = 1) underwent navigation-assisted resections. The mean values were 0.67 ± 0.15 mm (range, 0.47 to 0.97 mm) for FRE and 0.83 ± 0.51 mm (range, 0.42 to 2.28 mm) for TRE. Registration was successful in all cases. The mean time for CBCT imaging and tracker registration was 7.5 minutes. Conclusions: We present a novel automatic registration method for CAS exploiting intraoperative CBCT capabilities, which provided improved accuracy and reduced operative times compared with more traditional methods. Clinical Relevance: This proof-of-principle study validated a novel process for automatic registration to improve the accuracy of resecting bone tumors using a surgical navigation 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".