Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks | Zendy

Julia Krüger | Zendy; Roland Opfer | Zendy; Nils Gessert | Zendy; AnnChristin Ostwaldt | Zendy; Praveena Manogaran | Zendy; Hagen H. Kitzler | Zendy; Alexander Schlaefer | Zendy; Sven Schippling | Zendy

Open Access

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

Author(s) -

Julia Krüger,

Roland Opfer,

Nils Gessert,

AnnChristin Ostwaldt,

Praveena Manogaran,

Hagen H. Kitzler,

Alexander Schlaefer,

Sven Schippling

Publication year - 2020

Publication title -

neuroimage clinical

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.772

H-Index - 68

ISSN - 2213-1582

DOI - 10.1016/j.nicl.2020.102445

Subject(s) - segmentation , convolutional neural network , artificial intelligence , pattern recognition (psychology) , computer science , fluid attenuated inversion recovery , deep learning , encoder , feature (linguistics) , magnetic resonance imaging , medicine , radiology , linguistics , philosophy , operating system

Highlights • A fully automated segmentation of new or enlarged multiple sclerosis (MS) lesions.• 3D convolutional neural network (CNN) with U-net-like encoder-decoder architecture.• Simultaneous processing of baseline and follow-up scan of the same patient.• Trained on 3253 patient data from over 103 different MR scanners.• Fast (<1min), robust algorithm with segmentation results in inter-rater variability.

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