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

•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...

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Veröffentlicht in:NeuroImage clinical 2020-01, Vol.28, p.102445-102445, Article 102445
Hauptverfasser: Krüger, Julia, Opfer, Roland, Gessert, Nils, Ostwaldt, Ann-Christin, Manogaran, Praveena, Kitzler, Hagen H., Schlaefer, Alexander, Schippling, Sven
Format: Artikel
Sprache:eng
Schlagworte:
Convolutional neural network
Lesion activity
Lesion segmentation
Multiple sclerosis
Regular
U-net
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Beschreibung
Zusammenfassung:•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 (
ISSN:2213-1582
2213-1582
DOI:10.1016/j.nicl.2020.102445