Abstract
The automated analysis of medical images requires robust and accurate algorithms that address the inherent challenges of identifying heterogeneous anatomical and pathological structures, such as brain tumors, in large volumetric images. In this paper, we present Cerberus, a single lightweight convolutional neural network model for the segmentation of fine-grained brain tumor regions in multichannel MRIs. Cerberus has an encoder-decoder architecture that takes advantage of a shared encoding phase to learn common representations for these regions and, then, uses specialized decoders to produce detailed segmentations. Cerberus learns to combine the weights learned for each category to produce a final multi-label segmentation. We evaluate our approach on the official test set of the Brain Tumor Segmentation Challenge 2020, and we obtain dice scores of 0.807 for enhancing tumor, 0.867 for whole tumor and 0.826 for tumor core.
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Daza, L., Gómez, C., Arbeláez, P. (2021). Cerberus: A Multi-headed Network 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_30
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DOI: https://doi.org/10.1007/978-3-030-72087-2_30
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