Convolutional Neural Networks for Brain Tumor Segmentation Using Different Sets of MRI Sequences

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In clinical practice, multi-sequence MRI protocols for brain tumor segmentation are not standardized and therefore a flexible segmentation approach is needed which makes optimal use of all available MRI data. In this study, we present and evaluate an early and late fusion Convolutional Neural Network (CNN) based on DeepMedic architecture to segment brain tumor using different combinations of multi-sequence MRI datasets. While for the early fusion approach, we trained a dedicated CNN for all possible combinations of MRI sequences, the late fusion approach is a more generic approach where we trained an independent CNN for each type of MRI sequence and merged the feature maps using a fully connected layer to generate the final segmentation. Compared to an early fusion CNN, the segmentation performance of the late fusion approach was very similar while it provides more flexibility in terms of combining all available MRI data.