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A Segmentation-Aware Deep Fusion Network for Compressed Sensing MRI

  • Zhiwen Fan
  • Liyan Sun
  • Xinghao DingEmail author
  • Yue Huang
  • Congbo Cai
  • John Paisley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11210)

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 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 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.

Keywords

Compressed sensing Magnetic resonance imaging Medical image segmentation Deep neural network 

Supplementary material

474211_1_En_4_MOESM1_ESM.pdf (675 kb)
Supplementary material 1 (pdf 675 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhiwen Fan
    • 1
  • Liyan Sun
    • 1
  • Xinghao Ding
    • 1
    Email author
  • Yue Huang
    • 1
  • Congbo Cai
    • 1
  • John Paisley
    • 2
  1. 1.Fujian Key Laboratory of Sensing and Computing for Smart CityXiamen UniversityXiamenChina
  2. 2.Department of Electrical EngineeringColumbia UniversityNew YorkUSA

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