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 deep-based 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 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.

本文提出一种名为 SADFN 的分割感知深度融合网络，采用多层特征聚合方法融合了 MRI 数据集中各个层次的特征，提高了图像重建的准确性，经大量实验证明该方法在压缩感知 MRI 图像重建领域取得了最好的结果。