5 Résultats et discussion
5.5 Comparaison et discussion
Afin d’évaluer l’impact de la taille de l’ensemble de formation sur les résultats, nous testons de nouveau notre algorithme contre l’ensemble SKI10. Cette fois, nous choisissons quatre images aléatoires de l’ensemble SKI10, dont trois sont utilisées pour entraîner l’algorithme et une comme ensemble de test. Nous appelons cette collection l’ensemble « Tiny SKI10 ». Dans ce test, notre objectif est de confirmer que, si notre algorithme s’entraîne avec un ensemble d’entraînement similaire à SKI10 concernant la taille (60 images), nous nous attendons à ce que les résultats soient plutôt bons pour notre algorithme. Le tableau 13 montre les résultats obtenus à l’aide d’une collection de trois images de l’ensemble SKI10 en tant qu’ensemble d’entraînement et une seule image en tant que test.
Le déclin énorme dans la qualité des résultats obtenus à l’aide de l’ensemble « Tiny SKI10 », ainsi que l’utilisation d’un ensemble d’entraînement de trois images similaires à partir de l’ensemble BodyCad améliore ce résultat, il est supposé que si nous entraînons la version étendue de notre algorithme avec un ensemble plus grand, nous pourrions nous attendre à ce que l’algorithme produise de meilleurs résultats qu’il produit en utilisant l’ensemble d’entraînement complet de SKI10.
Tableau 13 – Résultats obtenus en utilisant quatre images de l’ensemble SKI10 optimisées
Cartilage Paramètre Valeur (moyenne de l'ensemble)
Fémoral DSC 0.6114 IOU 44.17 % Précision 96.52 % Sensibilité 48.39 % Spécificité 99.44 % Temps de calcul 3.01 s Tibial DSC 0.6006 IOU 43.05 % Précision 96.42 % Sensibilité 47.53 % Spécificité 99.39 % Temps de calcul 3.36 s
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Les données présentées dans le tableau 10 confirment que la version préliminaire de notre algorithme fonctionne presque aussi bien que les meilleurs algorithmes qui ont participé aux défis SKI10. En plus, le fait que la spécificité montrée dans le tableau 12 soit la spécificité maximale produite au cours de cette étude suggère que l'ajout de l'image secondaire a un impact énorme sur la qualité des résultats. Enfin, la différence entre les valeurs des tableaux 13 et 6 suggère que, en segmentant des images IRM supplémentaires et en les insérant dans l'ensemble d’entraînement BodyCad, nous pouvons nous attendre à ce que l'algorithme produise de meilleurs résultats que ce qui est produit en utilisant l’ensemble SKI10.
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