Deep learning for automatic segmentation of thigh and leg muscles

Introduction. This database includes the raw data linked with paper “Deep learning for automatic segmentation of thigh and leg muscles”.

In this paper, we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach using as input for the neural network MRI scans of subjects affected by facioscapulohumeral dystrophy and by amyotrophic lateral sclerosis.

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 (in brief). 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).