  Fully convolutional neural networks (CNNs) have proven to be effective at
representing and classifying textural information, thus transforming image
intensity into output class masks that achieve semantic image segmentation. In
medical image analysis, however, expert manual segmentation often relies on the
boundaries of anatomical structures of interest. We propose boundary aware CNNs
for medical image segmentation. Our networks are designed to account for organ
boundary information, both by providing a special network edge branch and
edge-aware loss terms, and they are trainable end-to-end. We validate their
effectiveness on the task of brain tumor segmentation using the BraTS 2018
dataset. Our experiments reveal that our approach yields more accurate
segmentation results, which makes it promising for more extensive application
to medical image segmentation.
