A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor Segmentation

Glioblastoma (GBM) is one of the leading causes of cancer death. The imaging diagnostics are critical for all phases in the treatment of brain tumor. However, manually-checked output by a radiologist has several limitations such as tedious annotation, time consuming and subjective biases, which influence the outcome of a brain tumor affected region. Therefore, the development of an automatic segmentation framework has attracted lots of attention from both clinical and academic researchers. Recently, most state-of-the-art algorithms are derived from deep learning methodologies such as the U-net, attention network. In this paper, we propose a deep supervised U-Attention Net framework for pixel-wise brain tumor segmentation, which combines the U-net, Attention network and a deep supervised multistage layer. Subsequently, we are able to achieve a low resolution and high resolution feature representations even for small tumor regions. Preliminary results of our method on training data have mean dice coefficients of about 0.75, 0.88, and 0.80; on the other hand, validation data achieve a mean dice coefficient of 0.67, 0.86, and 0.70, for enhancing tumor (ET), whole tumor (WT), and tumor core (TC) respectively .