Deep learning for automatic segmentation of thigh and leg muscles

Abramo Agosti, Enea Shaqiri, Matteo Paoletti, Francesca Solazzo, Niels Bergsland, Giulia Colelli, Giovanni Savini, Shaun I. Muzic, Francesco Santini, Xeni Deligianni, Luca Diamanti, Mauro Monforte, Giorgio Tasca, Enzo Ricci, Stefano Bastianello, Anna Pichiecchio

Risultato della ricerca: Contributo in rivistaArticolo in rivista


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.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
Stato di pubblicazionePubblicato - 2021


  • Deep learning
  • Magnetic resonance imaging
  • Muscle segmentation


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