Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss
verfasst von : Andrew Jesson, Tal Arbel
Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In this work, we use a 3D Fully Connected Network (FCN) architecture for brain tumor segmentation. Our method includes a multi-scale loss function on predictions given at each resolution of the FCN. Using this approach, the higher resolution features can be combined with the initial segmentation at a lower resolution so that the FCN models context in both the image and label domains. The model is trained using a multi-scale loss function and a curriculum on sample weights is employed to address class imbalance. We achieved competitive results during the testing phase of the BraTS 2017 Challenge for segmentation with Dice scores of 0.710, 0.860, and 0.783 for enhancing tumor, whole tumor, and tumor core, respectively.