Article open access publication

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI

Medical Image Analysis, Elsevier, ISSN 1361-8415

Volume 51, 2018

DOI:10.1016/j.media.2018.10.008, Dimensions: pub.1107841677, PMID: 30390514,

Affiliations

Organisations

  1. (1) Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC - University Medical Center Rotterdam, The Netherland. Electronic address: floriandubost1@gmail.com.
  2. (2) Erasmus MC, grid.5645.2
  3. (3) Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC - University Medical Center Rotterdam, The Netherland.
  4. (4) Delft University of Technology, grid.5292.c
  5. (5) University of Copenhagen, grid.5254.6, KU

Countries

Netherlands

Denmark

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Europe

Description

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automated EPVS scoring correlates similarly to age as visual scoring.

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University of Copenhagen

Danish Open Access Indicator

2019: Realized

Research area: Science & Technology

Danish Bibliometrics Indicator

2019: Level 2

Research area: Science & Technology

Dimensions Citation Indicators

Times Cited: 18

Relative Citation ratio (RCR): 1.61

Open Access Info

Green, Submitted