SRAD and optical flow based external energy for echocardiograms with primitive shape priors
Bibliographic record
Abstract
Accurate left ventricle border delineation is highly desirable on inherently noisy clinical echocardiograms. Active contour (or snake) is a powerful model-based image segmentation approach. In this work, we propose to use a modified gradient vector flow (GVF) snake to segment noisy echocardiographic image cycles. The first modification is to use a speckle reducing anisotropic diffusion (SRAD) operator to reduce the inherent speckle noise within the image. The second modification is to utilize the movement of the vessels and tissues (identified by means of optical flow analysis) and incorporate it into the external energy of the GVF snake. This will provide the necessary structural information while ignoring static noise prone areas of the image cine. Finally, the incorporation of an iterative priori knowledge process into the proposed solution will retract an expanding curve or correct a caving one when an expected border is occluded by noise. Results are compared with expert-defined segmentations yielding better sensitivity, precision rate and overlap ratio than that of the standard GVF model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".