Comparative evaluation of sparse and minimal data point cloud registration: A study on Tibiofemoral Bones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An accurate bone registration is a crucial step in Computer-assisted Orthopaedic Surgery (CAOS) to estimate the relationship between a preoperative patient’s bone model and the actual position during surgery. A-mode ultrasound and motion capture system is a new promising non-invasive technique to determine the bone’s 3D pose. The main challenge with such a system is the sparsity of the measurement; it could trap the optimization, which minimizes the registration error, in the local minima. In this paper, we aim to find the registration algorithm that could provide enough surgical navigation accuracy. Several registration algorithms were compared using Monte Carlo simulations. The number of points and placement sensitivity were also investigated while keeping the practical aspect of the system. With 15 points, Unscented Kalman Filter (UKF)-based registration with 6D similarity vector showed superior to the other examined algorithms in minimizing the transformation error. In terms of balancing the accuracy and the equipment availability, the simulation showed that points needed to be dispersedly placed; 15 points were sufficient to register the femur, but 20 points were required to register the tibia. Beyond this number, the registration error hardly improved and will therefore be used to base our number of sensors on. • Ultrasound and motion capture offer non-invasive skeletal kinematics estimation. • Unscented Kalman Filter with 6D vector surpasses other registration algorithms. • Adding normal measurements improves point correspondence search effectiveness. • Except internal-external rotation, accuracy < 1 mm/degree achieved in simulation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it