Journal
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
Volume 35, Issue 3, Pages 467-483Publisher
SPRINGER
DOI: 10.1007/s10334-021-00967-4
Keywords
Deep learning; Muscle segmentation; Magnetic resonance imaging
Funding
- Italian Ministry of Health [RF 2016-02362914]
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This study successfully implemented automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach, achieving high accuracy. The results hold promise for application to different skeletal muscle groups and other MRI sequences with varying contrast and resolution.
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.
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