Cited 3 time in
Cited 3 time in
Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network
- Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network
- Jung, Euijin; Chikontwe, Philip; Zong, Xiaopeng; Lin, Weili; Shen, Dinggang; Park, Sang Hyun
- DGIST Authors
- Park, Sang Hyun
- Issue Date
- IEEE Access, 7, 18382-18391
- Article Type
- Author Keywords
- Perivascular spaces; MRI enhancement; deep convolutional neural network; densely connected network; skip connections
- MRI; VISUALIZATION; DISEASE; MARKER; MODEL; VIRCHOW-ROBIN SPACES; SEGMENTATION; BRAIN
- 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.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Park, Sang Hyun
Medical Image & Signal Processing Lab
컴퓨터비전, 인공지능, 의료영상처리
- Department of Robotics EngineeringMedical Image & Signal Processing Lab1. Journal Articles
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.