Intra-subject Elastic Registration of 3D Ultrasound Images
Bibliographic record
Abstract
b School of Computing, Queen’s University, Canada. 3D registration of ultrasound images is an important and fast-growing research area with various medical applications, such as image-guided radiotherapy and surgery. However, this registration process remains extremely challenging due to the deformation of soft tissue and the existence of speckles in these images. This paper presents a technique for intra-subject, intra-modality elastic registration of 3D ultrasound images. Using the general concept of attribute vectors, we define the corresponding voxels in the fixed and moving images. Our method does not require presegmentation and does not employ any numerical optimization procedure. As the computational requirements are minimal, the method has potential use in real-time applications. The technique is implemented and tested on 3D ultrasound images of human liver, captured by a 3D ultrasound transducer. The results show that the method is sufficiently accurate and robust even in cases where artifacts such as shadows exist in the ultrasound data. Key words: Image registration, 3D ultrasound, elastic registration, soft-tissue deformation, attribute vector. 1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".