Phantom validation for ultrasound to statistical shape model registration of human pelvis
Bibliographic record
Abstract
Total Hip Replacement (THR) has become a common surgical procedure in recent years, as a result of increasing aging population with osteoarthritis of the hip joint. Localization of the pelvic anatomical coordinate system (PaCS) is a critical step in accurate placement of the femur prosthesis in the acetabulum in THR. Intra-operative ultrasound (US) imaging can provide a radiation-free navigation system for localization of the PaCS. However, US images are noisy and cannot provide any anatomical information beneath the bone surface due to the total reflection of US beam at the bone-soft tissue interface. A solution to this problem is to fuse intra-operative US with pre-operative imaging or a statistical shape model (SSM) of the pelvis. Here, we propose a multi-slice to volume intensity-based registration of the pelvic SSM to a sparse set of 2D US images in order to localize the PaCS in the US. In this registration technique, a set of 2D slices are extracted from a pelvic SSM using the approximate location and orientation of their corresponding 2D US images. During the registration, the comparison between the SSM slices and the US images is made using an ultrasound simulation technique and a correlation-based similarity metric. We demonstrate the feasibility of our proposed approach in localizing the PaCS on five patient-based phantoms. These results indicate the necessity of including pubic symphysis landmarks in the 2D US slices in order to have a precise estimation of the PaCS.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".