Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

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

doi:10.1109/ACCESS.2019.2896911

Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

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

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Article › peer-review

16 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 language	English
Article number	8632900
Pages (from-to)	18382-18391
Number of pages	10
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2019.2896911
Publication status	Published - 2019

Keywords

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

ASJC Scopus subject areas

Computer Science(all)
Materials Science(all)
Engineering(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ACCESS.2019.2896911

Cite this

@article{c1562145f62942eab4f129acdcd5b4bd,

title = "Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network",

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.",

keywords = "MRI enhancement, Perivascular spaces, deep convolutional neural network, densely connected network, skip connections",

author = "Euijin Jung and Philip Chikontwe and Xiaopeng Zong and Weili Lin and Dinggang Shen and Park, {Sang Hyun}",

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: {\textcopyright} 2013 IEEE.",

year = "2019",

doi = "10.1109/ACCESS.2019.2896911",

language = "English",

volume = "7",

pages = "18382--18391",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

AU - Jung, Euijin

AU - Chikontwe, Philip

AU - Zong, Xiaopeng

AU - Lin, Weili

AU - Shen, Dinggang

AU - Park, Sang Hyun

N1 - 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.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - MRI enhancement

KW - Perivascular spaces

KW - deep convolutional neural network

KW - densely connected network

KW - skip connections

UR - http://www.scopus.com/inward/record.url?scp=85062226110&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2019.2896911

DO - 10.1109/ACCESS.2019.2896911

M3 - Article

AN - SCOPUS:85062226110

SN - 2169-3536

VL - 7

SP - 18382

EP - 18391

JO - IEEE Access

JF - IEEE Access

M1 - 8632900

ER -

Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this