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A novel segmentation framework dedicated to the follow‐up of fat infiltration in individual muscles of patients with neuromuscular disorders
Author(s) -
Ogier Augustin C.,
Heskamp Linda,
Michel Constance P.,
Fouré Alexandre,
Bellemare MarcEmmanuel,
Le Troter Arnaud,
Heerschap Arend,
Bendahan David
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28030
Subject(s) - segmentation , intraclass correlation , reproducibility , sørensen–dice coefficient , computer science , robustness (evolution) , coefficient of variation , pattern recognition (psychology) , image segmentation , medicine , artificial intelligence , mathematics , statistics , biology , biochemistry , gene
Purpose To propose a novel segmentation framework that is dedicated to the follow‐up of fat infiltration in individual muscles of patients with neuromuscular disorders. Methods We 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. Results High 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. Conclusion The 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.