Segmentation d’image échotomographique par régions actives géodésiques
Why this work is in the frame
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Bibliographic record
Abstract
Dans cette etude nous nous interessons a la segmentation d’images ultrasonores vasculaire in vivo. Nous developpons une methode de segmentation utilisant, d’une facon originale, le modele de regions actives geodesiques. Cette approche tient compte a la fois des informations contours et regions. Nous exploitons les proprietes statistiques de ces informations. Pour cela l’information region est approchee par un modele de distribution de niveaux de gris de la region. La recherche des contours est faite par la methode des ensembles de niveaux a partir d’une courbe initiale. Nous avons teste notre algorithme sur des images ultrasonores reelles, images echotomographiques veineuses in vivo presentant un thrombus que nous cherchons a isoler. Les resultats experimentaux obtenus illustrent bien les bonnes performances de notre algorithme pour detecter et localiser le thrombus, et montrent aussi que notre methode est bien adaptee a la segmentation des images ultrasonores vasculaire. English version:In this paper a new segmentation method for ultrasound vascular images is developed, applying a geodesic active region model. This approach takes into account both boundary and region information. The region information are approached with a gray level distribution model. The evolution of initial curve has been implemented using a level set method. The algorithm was tested in vivo on real B-mode ultrasound images, to isolate the thrombus in venous ultrasound images. The experimental results confirmed the relevance of this approach to detect and locate the thrombus, and showed also that the method is adapted to ultrasound vascular images.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it