Abstract
In this paper we described our approach for glioma segmentation in multi-sequence magnetic resonance imaging (MRI) in the context of the MICCAI 2020 Brain Tumor Segmentation Challenge (BraTS). We proposed an architecture based on U-Net with a new computational unit termed “SE Norm” that brought significant improvements in segmentation quality. Our approach obtained competitive results on the validation (Dice scores of 0.780, 0.911, 0.863) and test (Dice scores of 0.805, 0.887, 0.843) sets for the enhanced tumor, whole tumor and tumor core sub-regions. The full implementation and trained models are available at https://github.com/iantsen/brats.
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Iantsen, A., Jaouen, V., Visvikis, D., Hatt, M. (2021). Squeeze-and-Excitation Normalization for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_32
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