Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

Euijin Jung, Philip Chikontwe, Xiaopeng Zong, Weili Lin, Dinggang Shen, Sang Hyun Park

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Perivascular spaces (PVS) in the human brain are related to various brain diseases. However, it is difficult to quantify them due to their thin and blurry appearance. In this paper, we introduce a deep-learning-based method, which can enhance a magnetic resonance (MR) image to better visualize the PVS. To accurately predict the enhanced image, we propose a very deep 3D convolutional neural network that contains densely connected networks with skip connections. The proposed networks can utilize rich contextual information derived from low-level to high-level features and effectively alleviate the gradient vanishing problem caused by the deep layers. The proposed method is evaluated on 17 7T MR images by a twofold cross-validation. The experiments show that our proposed network is much more effective to enhance the PVS than the previous PVS enhancement methods.

Original languageEnglish
Article number8632900
Pages (from-to)18382-18391
Number of pages10
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education under Grant 2018R1D1A1B07044473, and in part by the Grant of Artificial Intelligence Bio-Robot Medical Convergence Technology funded by the Ministry of Trade, Industry and Energy, the Ministry of Science and ICT, and the Ministry of Health and Welfare, under Grant 20001533.

Publisher Copyright:
© 2013 IEEE.

Keywords

  • MRI enhancement
  • Perivascular spaces
  • deep convolutional neural network
  • densely connected network
  • skip connections

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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