Automated Brain Tumour Segmentation Using Deep Fully Convolutional Residual Networks
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Automated brain tumour segmentation has the potential of making a massive improvement in disease diagnosis, surgery, monitoring and surveillance. However, this task is extremely challenging. Here, we describe our automated segmentation method using 2D CNNs that are based on U-Net. To deal with class imbalance effectively, we have formulated a weighted Dice loss function. We found that increasing the depth of the 'U' shape beyond a certain level results in a decrease in performance, so it is essential to choose an optimum depth. We also found that 3D contextual information cannot be captured by a single 2D network that is trained with patches extracted from multiple views whereas an ensemble of three 2D networks trained in multiple views can effectively capture the information and deliver much better performance. Our method obtained Dice scores of 0.79 for enhancing tumour, 0.90 for whole tumour, and 0.82 for tumour core on the BraTS 2018 validation set and its performance is comparable to the state-of-the-art methods.
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