  In this paper, we propose a dual aggregation network to adaptively aggregate
different information in infant brain MRI segmentation. More precisely, we
added two modules based on 3D-UNet to better model information at different
levels and locations. The dilated convolution pyramid downsampling module is
mainly to solve the problem of loss of spatial information on the downsampling
process, and it can effectively save details while reducing the resolution. The
self-attention module can integrate the remote dependence on the feature maps
in two dimensions of spatial and channel, effectively improving the
representation ability and discriminating ability of the model. Our results are
compared to the winners of iseg2017's first evaluation, the DICE ratio of WM
and GM increased by 0.7%, and CSF is comparable.In the latest evaluation of the
iseg-2019 cross-dataset challenge,we achieve the first place in the DICE of WM
and GM, and the DICE of CSF is second.
