Abstract
The work presented in this paper addresses the MICCAI BraTS 2019 challenge devoted to brain tumor segmentation using magnetic resonance images. For each task of the challenge, we proposed and submitted for evaluation an original method. For the tumor segmentation task (Task 1), our convolutional neural network is based on a variant of the U-Net architecture of Ronneberger et al. with two modifications: first, we separate the four convolution parts to decorrelate the weights corresponding to each modality, and second, we provide volumes of size \(240 * 240 * 3\) as inputs in these convolution parts. This way, we profit of the 3D aspect of the input signal, and we do not use the same weights for separate inputs. For the overall survival task (Task 2), we compute explainable features and use a kernel PCA embedding followed by a Random Forest classifier to build a predictor with very few training samples. For the uncertainty estimation task (Task 3), we introduce and compare lightweight methods based on simple principles which can be applied to any segmentation approach. The overall performance of each of our contribution is honorable given the low computational requirements they have both for training and testing.
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We would like thank NVidia Corporation for their Quadro P6000 GPU donation.
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Boutry, N., Chazalon, J., Puybareau, E., Tochon, G., Talbot, H., Géraud, T. (2020). Using Separated Inputs for Multimodal Brain Tumor Segmentation with 3D U-Net-like Architectures. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_18
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