Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation
Murugesan, G. K.; Nalawade, S.; Bangalore Yogananda, C. G.; Wagner, B.; Fei, B.; Madhuranthakam, A.; Maldjian, J. A.
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In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components, and then ensembled them to obtain robust segmentation maps. This reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm (xgboost) with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrasted this method can achieve excellent performance with DICE scores of 0.898, 0.784, 0.779 for whole tumor, tumor core and enhancing tumor respectively and accuracy 34.5 % for survuval prediction.
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