Does contextual information improve 3D U-Net based brain tumor segmentation?
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Effective, robust and automatic tools for brain tumor segmentation are needed for extraction of information useful in treatment planning. In recent years, convolutional neural networks have shown state-of-the-art performance in the identification of tumor regions in magnetic resonance (MR) images. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy. In this work it is instead investigated if the addition of contextual information in the form of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) masks improves U-Net based brain tumor segmentation. The BraTS 2020 dataset was used to train and test a standard 3D U-Net model that, in addition to the conventional MR image modalities, used the contextual information as extra channels. For comparison, a baseline model that only used the conventional MR image modalities was also trained. Dice scores of 80.76 and 79.58 were obtained for the baseline and the contextual information models, respectively. Results show that there is no statistically significant difference when comparing Dice scores of the two models on the test dataset p > 0.5. In conclusion, there is no improvement in segmentation performance when using contextual information as extra channels.
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