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

1 Scopus citations

Abstract

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
Volume53
Issue number5
DOIs
StatePublished - May 2021

Keywords

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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction'. Together they form a unique fingerprint.

Cite this