In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 9 ( 2022-9-6), p. e0274212-
Abstract:
Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0274212
DOI:
10.1371/journal.pone.0274212.g001
DOI:
10.1371/journal.pone.0274212.g002
DOI:
10.1371/journal.pone.0274212.g003
DOI:
10.1371/journal.pone.0274212.g004
DOI:
10.1371/journal.pone.0274212.g005
DOI:
10.1371/journal.pone.0274212.g006
DOI:
10.1371/journal.pone.0274212.g007
DOI:
10.1371/journal.pone.0274212.g008
DOI:
10.1371/journal.pone.0274212.g009
DOI:
10.1371/journal.pone.0274212.g010
DOI:
10.1371/journal.pone.0274212.t001
DOI:
10.1371/journal.pone.0274212.t002
DOI:
10.1371/journal.pone.0274212.t003
DOI:
10.1371/journal.pone.0274212.t004
DOI:
10.1371/journal.pone.0274212.t005
DOI:
10.1371/journal.pone.0274212.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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