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

Pietro Maggi, Mário João Fartaria, João Jorge, Francesco La Rosa, Martina Absinta, Pascal Sati, Reto Meuli, Renaud Du Pasquier, Daniel S. Reich, Meritxell Bach Cuadra, Cristina Granziera, Jonas Richiardi, Tobias Kober

Research output: Contribution to journalArticle

Abstract

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 ~ 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.

Original languageEnglish (US)
Article numbere4283
JournalNMR in biomedicine
Volume33
Issue number5
DOIs
StatePublished - May 1 2020

Keywords

  • Central vein sign
  • MS mimics
  • deep learning
  • multiple sclerosis

ASJC Scopus subject areas

  • Molecular Medicine
  • Radiology Nuclear Medicine and imaging
  • Spectroscopy

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  • Cite this

    Maggi, P., Fartaria, M. J., Jorge, J., La Rosa, F., Absinta, M., Sati, P., Meuli, R., Du Pasquier, R., Reich, D. S., Cuadra, M. B., Granziera, C., Richiardi, J., & Kober, T. (2020). CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis. NMR in biomedicine, 33(5), [e4283]. https://doi.org/10.1002/nbm.4283