Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 5th International Wo Alessandro Crimi,Spyridon Bakas Conferen

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F.M. Veronese,G. Pasut,S. Drioli,G.M. Bonoralioblastoma (GBM) brain tumor segmentation with Cascaded U-Net. Training patches are extracted from 335 cases from Brain Tumor Segmentation (BraTS) Challenge for training and results are validated on 125 patients. The proposed approach is evaluated quantitatively in terms of Dice Similarity Coefficient (DSC) and Hausdorff95 distance.

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Norbert Welsch,Frank von Kuhlbergour image segmentation. Based on the ensembled segmentation, we present a biophysics-guided prognostic model for patient overall survival prediction which outperforms a data-driven radiomics approach. Our method won the second place of the MICCAI 2019 BraTS Challenge for the overall survival prediction.

MUTED

Jöns G. Hilborn,P. Dubois,W. Volksennce with DICE scores of 0.898, 0.784, 0.779 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively and an accuracy of 34.5% for predicting survival. The Ensemble of multiresolution 2D networks achieved 88.75%, 83.28% and 79.34% dice for WT, TC, and ET respectively in a test dataset of 166 subjects.

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opinionated

The Role of Lysine-7 in Ribonuclease-Ak. We supervise our network with a variant of the focal Tversky loss function. Our architecture promotes explain-ability, light-weight CNN design and has achieved 0.687, 0.843 and 0.751 DSC scores on the BraTs 2019 test cohort which is competitive with the commonly used vanilla U-Net.

aphasia

Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Predictionour image segmentation. Based on the ensembled segmentation, we present a biophysics-guided prognostic model for patient overall survival prediction which outperforms a data-driven radiomics approach. Our method won the second place of the MICCAI 2019 BraTS Challenge for the overall survival prediction.