In Journal of magnetic resonance imaging : JMRI
BACKGROUND : Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning-based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible.
PURPOSE : To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies.
STUDY TYPE : Retrospective.
SUBJECTS : 24 patients and 19 healthy controls.
FIELD STRENGTH/SEQUENCES : 3T; Interleaved 3D GRE.
ASSESSMENT : A 3D U-Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1- maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland-Altman and Pearson correlation by comparing FF values between manual and automated methods.
STATISTICAL TESTS : PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland-Altman analysis and the Pearson's coefficient (r2 ).
RESULTS : DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, -0.56] and 0.989 in thigh and [0.84, -0.71] and 0.971 in the calf.
DATA CONCLUSION : Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies.
LEVEL OF EVIDENCE : 3 TECHNICAL EFFICACY STAGE: 1.
Chen Yongsheng, Moiseev Daniel, Kong Wan Yee, Bezanovski Alexandar, Li Jun
Dixon magnetic resonance imaging, axonal loss, convolutional neural network, fat fraction, muscle, peripheral neuropathy