In this work, we investigate the possibility to directly apply convolutional neural networks (CNN) to segmentation of brain tumor tissues. As input to the network, we use multi-channel intensity information from a small patch around each point to be labelled. Only standard intensity pre-processing is applied to the input data to account for scanner differences. No post-processing is applied to the output of the CNN. We report promising preliminary results on the high-grade training data from the BraTS 2013 challenge. Work for the final submission will include architecture modifications, parameter tuning and training on the BraTS 2014 training corpus.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this work, we investigate the possibility to directly apply convolutional neural networks (CNN) to segmentation of brain tumor tissues. As input to the network, we use multi-channel intensity information from a small patch around each point to be labelled. Only standard intensity pre-processing is applied to the input data to account for scanner differences. 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