, Méthode Pour déterminer le niveau d'abstraction optimal de ces deux architectures, qui dépend de l'application pour laquelle les réseaux sont entrainés, nous avons fait varier leur nombre de couches
, Celui-ci a progressivement été augmenté en partant d'un U-net 9 couches et d'un V-net 7 couches
, cette augmentation a été réalisée en ajoutant dans le bas du U : -un max-pooling
, Cette méthode est représentée sur la Figure.VI.15 où l'on passe d'un U-net 17 couches à un U-net
15 -Augmentation du nombre de couches (17 (a) à 24 (b)) de l'architecture U-net ,
,
, Combien de volumes d'entrainement sont nécessaires pour que U-net (24 couches) obtienne une bonne précision (Di ce > 0.8) sur l'ensemble d'entrainement
, Quelle est la meilleure stratégie pour améliorer la précision des segmentations : augmenter le nombre de volumes d
, Méthode Créer une base de données annotées nécessite beaucoup d'efforts et de temps, il est donc important d'avoir une idée du nombre de volumes d'entrainement nécessaire pour obtenir des segmentations précises et de savoir quelle stratégie utiliser pour améliorer la performance des réseaux
, VI.1.2) puis nous avons entrainé un réseau U-net (24 couches) en faisant varier le nombre de volumes dans la base d'entrainement. Lors de chaque entrainement, Pour évaluer cela, nous avons fixé un ensemble de validation et un ensemble de test (définis dans la partie
, Les paramètres utilisés pour optimiser U-net ont été décrits dans la partie.VI.3. Résultats et discussion L'évolution du Dice moyen (sur les 10 optimisations) en fonction du nombre de volumes dans la base d'entrainement est montré sur la Figure
24 -Évolution du Dice moyen obtenu sur les jeux d'entrainement et de test en fonction de la ,
Le premier cas était un cas d'hydrocéphalie et le second une acquisition réalisée avec une sonde Philips PureWave sur un patient dont l'une des corne occipitale était anormalement dilatée. Dans le premier cas, les performances des quatre réseaux ont été moins bonnes qu'en moyenne sur le jeu de test et la corne occipitale dilatée n'a pas été segmentée correctement. Des performances correct (Di ce > 0.7) on néanmoins été obtenues pour certains réseaux. Dans le second cas, aucun des quatre réseaux n'a pu segmenter le SVC avec précision, le meilleur Dice étant de 0.3. Ces résultats montrent que les réseaux n'ont pas la capacité de segmenter des SVC qui n'ont pas une topologie semblable à celles observées dans le jeu d'entrainement. En revanche, ils semblent être en mesure de segmenter correctement des données acquises avec une autre sonde. Ce dernier point devra néanmoins être vérifié sur d'autres acquisitions, mais la segmentation de données provenant d'une sonde différente semble être envisageable, deux cas atypiques ont été segmentés par les réseaux U-net et V-net avec et sans CPPN ,
U-net et V-net ont été optimisés pour segmenter le SVC et les thalami. Les résultats obtenus ont montrés que V-net était capable de segmenter les thalami avec une excellente précision : Di ce = 0.891 ± 0 ,
, Compte tenu de la difficulté de ces deux problèmes de segmentation et des bonnes performances obtenues par V-net, on peut s'attendre à ce que cette architecture permette d'obtenir de bon résultats pour d'autres structures cérébrales
, La première segmentation automatique des thalami dans des données échographiques 3D, dans un temps clinique
, La modification des architectures de U-net et V-net qui permet à ces réseaux d'encoder la position anatomique du SVC
L'estimation du nombre de volumes d'apprentissage nécessaires pour segmenter le SVC avec précision (Dice > 0.8) et la comparaison de plusieurs stratégies d'augmentation artificielle de données, 2019. ,
, Les points 1 et 4 ont été valorisés lors de présentations orales au congrès IUS, 2018.
Les points 1 et 3 ont été valorisés par un article soumis dans le journal MEDIA, 2019. ,
, Dans le cas de la segmentation du SVC, plusieurs points pourraient être étudiés : -Enrichir la base de données avec des cas d'hydrocéphalie pour apprendre aux réseaux à traiter ces cas
, Vérifier que des réseaux U-net et V-net, entrainés sur nos données, sont capable de segmenter des données échographiques 3D obtenues à partir d'acquisitions réalisées avec d'autres sondes
, En ce qui concerne les thalami, les points qui pourraient être étudiés sont : -Évaluer l'influence du CPPN, comme cela a été fait pour le SVC. -Déterminer si certaines stratégies d'augmentation artificielle de données permettent d'améliorer la précision des segmentations
, Déterminer si il est possible d'améliorer significativement la précision des segmentations en
, Enfin, la base de données pourraient être enrichie avec des segmentations de nouvelles structures, ce qui permettrait de vérifier que cette architecture est adéquate pour segmenter l'ensemble des structures cérébrales chez le prématuré. De plus, il serait intéressant d'optimiser ou de modifier celle-ci pour qu'elle soit dédiée à ce problème et soit la moins demandeuse en ressources (de manière à être utilisable sur la plupart des GPUs). Pour cela, intégrer le CPPN aux architectures est un bon point de départ car il parait important d'encoder la position anatomique des structures cérébrales pour faciliter leur segmentation
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