  Brain magnetic resonance (MR) segmentation for hydrocephalus patients is
considered as a challenging work. Encoding the variation of the brain
anatomical structures from different individuals cannot be easily achieved. The
task becomes even more difficult especially when the image data from
hydrocephalus patients are considered, which often have large deformations and
differ significantly from the normal subjects. Here, we propose a novel
strategy with hard and soft attention modules to solve the segmentation
problems for hydrocephalus MR images. Our main contributions are three-fold: 1)
the hard-attention module generates coarse segmentation map using
multi-atlas-based method and the VoxelMorph tool, which guides subsequent
segmentation process and improves its robustness; 2) the soft-attention module
incorporates position attention to capture precise context information, which
further improves the segmentation accuracy; 3) we validate our method by
segmenting insula, thalamus and many other regions-of-interests (ROIs) that are
critical to quantify brain MR images of hydrocephalus patients in real clinical
scenario. The proposed method achieves much improved robustness and accuracy
when segmenting all 17 consciousness-related ROIs with high variations for
different subjects. To the best of our knowledge, this is the first work to
employ deep learning for solving the brain segmentation problems of
hydrocephalus patients.
