Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Yongsheng Chen, Daniel Moiseev, Wan Yee Kong, Alexandar Bezanovski, Jun Li

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1539-1549
Number of pages11
JournalJournal of Magnetic Resonance Imaging
Issue number5
StatePublished - May 2021


  • axonal loss
  • convolutional neural network
  • Dixon magnetic resonance imaging
  • fat fraction
  • muscle
  • peripheral neuropathy

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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