Abstract. Compressed sensing MRI is a classic inverse problem in the
field of computational imaging, accelerating the MR imaging by measuring less k-space data. The deep neural network models provide the
stronger representation ability and faster reconstruction compared with
”shallow” optimization-based methods. However, in the existing deepbased CS-MRI models, the high-level semantic supervision information
from massive segmentation-labels in MRI dataset is overlooked. In this
paper, we proposed a segmentation-aware deep fusion network called
SADFN for compressed sensing MRI. The multilayer feature aggregation (MLFA) method is introduced here to fuse all the features from
different layers in the segmentation network. Then, the aggregated feature maps containing semantic information are provided to each layer in
the reconstruction network with a feature fusion strategy. This guarantees the reconstruction network is aware of the different regions in the
image it reconstructs, simplifying the function mapping. We prove the
utility of the cross-layer and cross-task information fusion strategy by
comparative study. Extensive experiments on brain segmentation benchmark MRBrainS and BratS15 validated that the proposed SADFN model
achieves state-of-the-art accuracy in compressed sensing MRI. This paper provides a novel approach to guide the low-level visual task using
the information from mid- or high-level task