  Accuracy segmentation of brain structures could be helpful for glioma and
radiotherapy planning. However, due to the visual and anatomical differences
between different modalities, the accurate segmentation of brain structures
becomes challenging. To address this problem, we first construct a residual
block based U-shape network with a deep encoder and shallow decoder, which can
trade off the framework performance and efficiency. Then, we introduce the
Tversky loss to address the issue of the class imbalance between different
foreground and the background classes. Finally, a model ensemble strategy is
utilized to remove outliers and further boost performance.
