Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention
This work addresses segmentation accuracy in infant brain MRI, which is crucial for medical diagnosis, but it is incremental as it builds on the 3D-UNet architecture.
The paper tackles infant brain MRI segmentation by proposing a dual aggregation network that improves spatial detail preservation and feature representation, resulting in a 0.7% increase in DICE ratio for WM and GM and achieving first place in the iseg-2019 challenge.
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.