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.
This work was supported by a Canadian Natural Science and Engineering Research Council Collaborative Research and Development Grant (CRDPJ 505357-16) and Synaptive Medical. We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan X Pascal GPU used for this research.
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Jesson, A., Arbel, T. (2018). Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss . In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_34
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DOI: https://doi.org/10.1007/978-3-319-75238-9_34
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