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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Zusammenfassung:	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 muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation. Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue. We develop a supervised deep learning approach based on a unified framework for ROI segmentation. Results The proposed network generates segmentation maps with high accuracy, consisting in Dice Scores ranging from 0.89 to 0.95, with respect to “ground truth” manually segmented labelled images, also showing high average performance in both mild and severe cases of disease involvement (i.e. entity of fatty replacement). Discussion The presented results are promising and potentially translatable to different skeletal muscle groups and other MRI sequences with different contrast and resolution.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1352-8661 0968-5243 1352-8661
DOI:	10.1007/s10334-021-00967-4