Statistical unit root test for edge detection in ultrasound images of vessels and cysts
Why this work is in the frame
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Bibliographic record
Abstract
A new approach is proposed for edge detection in ultrasound. The technique examines the image intensity profile for unit roots based on the Dickey–Fuller statistical test of stationarity. The existence of the unit root is a sign of nonstationarity and a possible edge. A simple algorithm to build a segmentation method based on this edge detection approach is also proposed, which is capable of delineating the perimeter of hollow structures such as blood vessels and cysts. In this approach, the radial edge profiles originating from the center of the object of interest are scanned for the change from stationary to nonstationary status. The algorithm treats the radial intensity profiles as a time series and uses the Dickey–Fuller statistical test along the radii to find the location at which the profile becomes nonstationary. A priori criteria for edge continuity, shape, and size of the object of interest are applied to enhance the stability of the algorithm. The accuracy is demonstrated on simulated ultrasound. Further, the method is examined on two different image sets of blood vessels and validated based on contours marked by experts. The worst case distance from expert contours is 1.8±0.3 mm . The average area difference between the expert and the extracted contours is ∼6% and ∼4% of the vessel area in the two datasets. The proposed segmentation method is also compared to segmentation using active contours on ultrasound images of breast and ovarian cysts and shown to be accurate and stable.
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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 | 0.000 |
| 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 it