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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.
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
URL: https://doi.org/10.1007/s10334-021-00967-4
DOI: 10.1007/s10334-021-00967-4
PubMed id: 34665370
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