Deep learning for automatic segmentation of thigh and leg muscles

Objective In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. Material and methods The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on m...

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Veröffentlicht in:	Magma (New York, N.Y.) Jg. 35; H. 3; S. 467 - 483
Hauptverfasser:	Agosti, Abramo, Shaqiri, Enea, Paoletti, Matteo, Solazzo, Francesca, Bergsland, Niels, Colelli, Giulia, Savini, Giovanni, Muzic, Shaun I., Santini, Francesco, Deligianni, Xeni, Diamanti, Luca, Monforte, Mauro, Tasca, Giorgio, Ricci, Enzo, Bastianello, Stefano, Pichiecchio, Anna
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.06.2022
Schlagworte:	Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Computer Appl. in Life Sciences Deep Learning Health Informatics Image Processing, Computer-Assisted - methods Imaging Leg - diagnostic imaging Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Muscle, Skeletal - diagnostic imaging Radiology Research Article Solid State Physics Thigh - diagnostic imaging Deep learning Magnetic resonance imaging Muscle segmentation
ISSN:	1352-8661, 0968-5243, 1352-8661
Online-Zugang:	Volltext
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