Accurate optical flow field estimation using mechanical properties of soft tissues
Bibliographic record
Abstract
A novel optical flow based technique is presented in this paper to measure the nodal displacements of soft tissue undergoing large deformations. In hyperelasticity imaging, soft tissues maybe compressed extensively [1] and the deformation may exceed the number of pixels ordinary optical flow approaches can detect. Furthermore in most biomedical applications there is a large amount of image information that represent the geometry of the tissue and the number of tissue types present in the organ of interest. Such information is often ignored in applications such as image registration. In this work we incorporate the information pertaining to soft tissue mechanical behavior (Neo-Hookean hyperelastic model is used here) in addition to the tissue geometry before compression into a hierarchical Horn-Schunck optical flow method to overcome this large deformation detection weakness. Applying the proposed method to a phantom using several compression levels proved that it yields reasonably accurate displacement fields. Estimated displacement results of this phantom study obtained for displacement fields of 85 pixels/frame and 127 pixels/frame are reported and discussed in this paper.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".