Fast‐marching segmentation of three‐dimensional intravascular ultrasound images: A pre‐ and post‐intervention study
Notice bibliographique
Résumé
PURPOSE: Intravascular ultrasound (IVUS) is a vascular imaging technique that is used to study atherosclerosis since it has the ability to show the lumen and the vessel wall. Cross-sectional images of blood vessels are produced and they provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions, as well as the plaque shape and size. Due to the ultrasound speckle, catheter artifacts, or calcification shadows, the automated analysis of large IVUS data sets represents an important challenge. METHODS: A multiple interface 3D fast-marching method is presented for the detection of the lumen and external vessel wall boundaries. The segmentation is based on a combination of region and contour information, namely, the gray level probability density functions of the vessel structures and the intensity gradient. The detection of the lumen boundary is fully automatic. The segmentation method includes an interactive initialization procedure of the external vessel wall border. The segmentation method was applied to 20 in vivo IVUS data sets acquired from femoral arteries. This database contained three subgroups: Pullbacks acquired before balloon angioplasty (n=7), after the intervention (n=7), and at a 1 yr follow-up examination (n=6). Results were compared to validation contours that were manually traced by two experts on more than 1500 individual frames. RESULTS: For all subgroups, no significant difference was found between the area measurements of the segmentation and validation contours for the lumen and external vessel wall. Moreover, high intraclass correlation coefficients (> 0.96) between the area of the manually traced contours and detected boundaries with the fast-marching method were obtained for both vessel layers over the whole database. The segmentation performance was also evaluated with point-to-point contour distances between segmentation results and manually traced contours. A good overall accuracy was obtained with average distances < 0.13 mm and maximum distances < 0.46 mm, indicating a good performance in regions lacking information or containing artifacts. Only small differences of less than a pixel (0.02 mm) were observed between the average distance metrics of each subgroup, which prove the segmentation consistency. CONCLUSIONS: This new IVUS segmentation method provides accurate results that correspond well to the experts' manually traced contours, but requires much less manual interactions and is faster.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».