Cited 3 time in webofscience Cited 3 time in scopus

Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

Title
Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network
Authors
Jung, EuijinChikontwe, PhilipZong, XiaopengLin, WeiliShen, DinggangPark, Sang Hyun
DGIST Authors
Park, Sang Hyun
Issue Date
2019-02
Citation
IEEE Access, 7, 18382-18391
Type
Article
Article Type
Article
Author Keywords
Perivascular spacesMRI enhancementdeep convolutional neural networkdensely connected networkskip connections
Keywords
MRIVISUALIZATIONDISEASEMARKERMODELVIRCHOW-ROBIN SPACESSEGMENTATIONBRAIN
ISSN
2169-3536
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 deeplearning-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.
URI
http://hdl.handle.net/20.500.11750/9614
DOI
10.1109/ACCESS.2019.2896911
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
Collection:
Department of Robotics EngineeringMedical Image & Signal Processing Lab1. Journal Articles


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