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  4. CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis
 
research article

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

Maggi, Pietro
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Fartaria, Mario Joao
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Jorge, Joao  
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March 3, 2020
Nmr In Biomedicine

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS+) and 448 CVS-negative (CVS-) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS+/CVS- lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was similar to 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.

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Type
research article
DOI
10.1002/nbm.4283
Web of Science ID

WOS:000517652000001

Author(s)
Maggi, Pietro
Fartaria, Mario Joao
Jorge, Joao  
La Rosa, Francesco  
Absinta, Martina
Sati, Pascal
Meuli, Reto
Du Pasquier, Renaud
Reich, Daniel S.
Cuadra, Meritxell Bach  
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Date Issued

2020-03-03

Published in
Nmr In Biomedicine
Article Number

e4283

Subjects

Biophysics

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Radiology, Nuclear Medicine & Medical Imaging

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Spectroscopy

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central vein sign

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deep learning

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ms mimics

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multiple sclerosis

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white-matter

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lesions

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mri

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diagnosis

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pathology

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brain

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CIBM-AIT

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIFMET  
CIBM  
LTS5  
Available on Infoscience
March 19, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/167428
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