  Segmentation of brain structures from magnetic resonance (MR) scans plays an
important role in the quantification of brain morphology. Since 3D deep
learning models suffer from high computational cost, 2D deep learning methods
are favored for their computational efficiency. However, existing 2D deep
learning methods are not equipped to effectively capture 3D spatial contextual
information that is needed to achieve accurate brain structure segmentation. In
order to overcome this limitation, we develop an Anatomical Context-Encoding
Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D
convolutional neural networks (CNNs) for efficient and accurate segmentation of
brain structures from MR scans, consisting of 1) an anatomical context encoding
module to incorporate anatomical information in 2D CNNs and 2) a spatial
context encoding module to integrate 3D image information in 2D CNNs. In
addition, a skull stripping module is adopted to guide the 2D CNNs to attend to
the brain. Extensive experiments on three benchmark datasets have demonstrated
that our method achieves promising performance compared with state-of-the-art
alternative methods for brain structure segmentation in terms of both
computational efficiency and segmentation accuracy.
