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

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

The quantification of new or enlarged lesions from follow-up MRI scans is an important surrogate of clinical disease activity in patients with multiple sclerosis (MS). Not only is manual segmentation time consuming, but inter-rater variability is high. Currently, only a few fully automated methods a...

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Bibliographic Details
Main Authors: Julia Krüger, Roland Opfer, Nils Gessert, Ann-Christin Ostwaldt, Praveena Manogaran, Hagen H. Kitzler, Alexander Schlaefer, Sven Schippling
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:NeuroImage: Clinical
Subjects:
Multiple sclerosis
Lesion activity
Convolutional neural network
U-net
Lesion segmentation
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158220302825

Internet

http://www.sciencedirect.com/science/article/pii/S2213158220302825

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