Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers
Bibliographic record
Abstract
Breast Magnetic Resonance Imaging (MRI) is a reliable imagingtool for localization and evaluation of lesions prior to breast conservingsurgery (BCS). MR images typically will be used to determinethe size and location of the tumours before making the incisionin order to minimize the amount of tissue excised.The arm position and configuration of the breast during andprior to surgery are different and one question is whether it wouldbe possible to match the two configurations. This matching processcan potentially be used in development of tools to guide surgeonsin the incision process.Recently, a Thin-Plate-Spline (TPS) algorithm has been proposedto assess the feasibility of breast tissue matching using fiducialsurface markers in two different arm positions. The registrationalgorithm uses the surface markers only and does not employ theimage intensities.In this manuscript, we apply and evaluate a coherent point drift(CPD) algorithm for registration of three-dimensional breast MR imagesof six patient volunteers. In particular, we evaluate the resultsof the previous TPS registration technique to the proposed rigidCPD, affine CPD, and deformable CPD registration algorithms onthe same patient datasets.The preliminary results suggest that the CPD deformable registrationalgorithm is superior in correcting the motion of the breastcompared to CPD rigid, affine and TPS registration algorithms.
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How this classification was reachedexpand
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 | 0.005 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".