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Deep learning for automatic segmentation of thigh and leg muscles

Lookup NU author(s): Professor Giorgio TascaORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2021, The Author(s). 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.

Publication metadata

Author(s): Agosti A, Shaqiri E, Paoletti M, Solazzo F, Bergsland N, Colelli G, Savini G, Muzic SI, Santini F, Deligianni X, Diamanti L, Monforte M, Tasca G, Ricci E, Bastianello S, Pichiecchio A

Publication type: Article

Publication status: Published

Journal: Magnetic Resonance Materials in Physics, Biology and Medicine

Year: 2022

Volume: 35

Issue: 3

Pages: 467-483

Print publication date: 01/06/2022

Online publication date: 19/10/2021

Acceptance date: 04/10/2021

Date deposited: 22/02/2023

ISSN (print): 0968-5243

ISSN (electronic): 1352-8661

Publisher: Springer Nature


DOI: 10.1007/s10334-021-00967-4

PubMed id: 34665370


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Funder referenceFunder name
RC 2017-2019
RF 2016-02362914
RC 2020