Focal Dice Loss and Image Dilation for Brain Tumor Segmentation

For accurate tumor segmentation in brain magnetic resonance (MR) images, the extreme class imbalance not only exists between the foreground and background, but among different sub-regions of tumor. Inspired by the focal loss [3] that down-weights the well-segmented classes, our proposed Focal Dice Loss (FDL) considers the imbalance among structures of interest instead of the entire image including background. Image dilation is applied to the training samples, which enlarges the tiny sub-regions, bridges the disconnected pieces of tumor structures and promotes understanding on overall tumor rather than complex details. The structuring element for dilation is gradually down-sized, resulting in a coarse-to-fine and incremental learning process with the structure of network unchanged. Our experiments on the BRATS2015 dataset achieves the state-of-the-art in Dice Coefficient on average with relatively low computational cost.