Experimental validation of an intrasubject elastic registration algorithm for dynamic‐3D ultrasound images
Bibliographic record
Abstract
PURPOSE: In image-guided therapy, real-time visualization of the anatomy and adjustments in the therapy plan due to anatomical motions during the procedure is of outmost importance. 3D ultrasound has the potential to enable this real-time monitoring; however, nonrigid registration of a sequence of 3D ultrasound volumes remains to be a challenging problem. The authors present our recent results on the development of a computationally inexpensive feature-based registration algorithm for elastic alignment of dynamic-3D ultrasound images. METHODS: Our algorithm uses attribute vectors, based on the image intensity and gradient information, to perform feature-based matching in a sequence of 3D ultrasound images. Prior information from both the fixed and previous moving images is utilized to track features throughout the 3D image series. The algorithm has been compared to various publicly available registration techniques, i.e., the B-splines deformable registration, the symmetric forces Demons, and the fast free-form deformable registration method. RESULTS: Using a series of validation experiments on datasets collected from carotid artery, liver, and kidney of 20 subjects, the authors demonstrate that the feature-based, B-splines, Demons, and fast free-form deformable registration techniques can all recover volume deformations in a 3D ultrasound image series with reasonable accuracy; however, the proposed feature-based registration technique has substantial computational advantage over the other approaches. CONCLUSIONS: The proposed feature-based registration technique has the potential for real-time implementation on a computationally inexpensive platform and has the capability of recovering nonrigid deformations in tissue with reasonable accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".