Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.

PurposeTo propose a novel segmentation framework that is dedicated to the follow‐up of fat infiltration in individual muscles of patients with neuromuscular disorders.MethodsWe designed a semi‐automatic segmentation pipeline of individual leg muscles in MR images based on automatic propagation through nonlinear registrations of initial delineation in a minimal number of MR slices. This approach has been validated for the segmentation of individual muscles from MRI data sets, acquired over a 10‐month period, from thighs and legs in 10 patients with muscular dystrophy. The robustness of the framework was evaluated using conventional metrics related to muscle volume and clinical metrics related to fat infiltration.ResultsHigh accuracy of the semi‐automatic segmentation (mean Dice similarity coefficient higher than 0.89) was reported. The provided method has excellent reliability regarding the reproducibility of the fat fraction estimation, with an average intraclass correlation coefficient score of 0.99. Furthermore, the present segmentation framework was determined to be more reliable than the intra‐expert performance, which had an average intraclass correlation coefficient of 0.93.ConclusionThe proposed framework of segmentation can successfully provide an effective and reliable tool for accurate follow‐up of any MRI biomarkers in neuromuscular disorders. This method could assist the quantitative assessment of muscular changes occurring in such diseases.