3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance...

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Veröffentlicht in:	Frontiers in neuroinformatics Jg. 15; S. 641600
Hauptverfasser:	Boutinaud, Philippe, Tsuchida, Ami, Laurent, Alexandre, Adonias, Filipa, Hanifehlou, Zahra, Nozais, Victor, Verrecchia, Violaine, Lampe, Leonie, Zhang, Junyi, Zhu, Yi-Cheng, Tzourio, Christophe, Mazoyer, Bernard, Joliot, Marc
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Research Foundation 18.06.2021 Frontiers Media Frontiers Media S.A
Schlagworte:	Algorithms Basal ganglia brain cohort Brain research Cognitive ability Deep learning Dementia Life Sciences Magnetic resonance imaging Medical imaging Medical research MRI Neural networks Neuroscience Performance evaluation perivascular space Santé publique et épidémiologie Segmentation Substantia alba Traumatic brain injury U-net Young adults perivascular space deep learning segmentation brain cohort MRI U-net Deep learning Brain cohort Segmentation Perivascular space
ISSN:	1662-5196, 1662-5196
Online-Zugang:	Volltext
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