3D curve constrained deformable registration using a neuro-fuzzy transformation model
Bibliographic record
Abstract
Image registration of abdominal organs and soft tissues is considered daunting due to large organ shift and tissue deformation caused by patient motion, respiration, etc. In this study, we propose a novel neuro-fuzzy deformable registration technique that is constrained by 3D curves of vessel centerlines and point marks while minimizing strain energy. We present an analytical global optimal solution in the case when 3D curves, strain energy and point marks are considered, which will provide fast and robust deformable match for internal structures such as blood vessels, and significantly reduce the chance to get trapped in local minima. We have demonstrated the effectiveness of our deformable technique in registering liver MR images. Validation shows a target registration error of 1.98 mm and an average centerline distance error of 1.65 mm. This technique has the potential to significantly improve registration capability and the quality of intra-operative image guidance.
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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 | 0.000 | 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 | 0.004 |
| 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".