Multicenter Validation of Automated Segmentation and Composition Analysis of Lumbar Paraspinal Muscles Using Multisequence MRI.

Journal: Radiology. Artificial intelligence
Published Date: Aug 20, 2025

Abstract

. Chronic low back pain is a global health issue with considerable socioeconomic burdens and is associated with changes in lumbar paraspinal muscles (LPM). In this retrospective study, a deep learning method was trained and externally validated for automated LPM segmentation, muscle volume quantification, and fatty infiltration assessment across multisequence MRIs. A total of 1,302 MRIs from 641 participants across five centers were included. Data from two centers were used for model training and tuning, while data from the remaining three centers were used for external testing. Model segmentation performance was evaluated against manual segmentation using the Dice similarity coefficient (DSC), and measurement accuracy was assessed using two one-sided tests and Intraclass Correlation Coefficients (ICCs). The model achieved global DSC values of 0.98 on the internal test set and 0.93 to 0.97 on external test sets. Statistical equivalence between automated and manual measurements of muscle volume and fat ratio was confirmed in most regions ( < .05). Agreement between automated and manual measurements was high (ICCs > 0.92). In conclusion, the proposed automated method accurately segmented LPM and demonstrated statistical equivalence to manual measurements of muscle volume and fatty infiltration ratio across multisequence, multicenter MRIs. ©RSNA, 2025.

Authors

  • Zhongyi Zhang
    Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Julie A Hides
    School of Allied Health, Sport and Social Work, Griffith University, Nathan, QLD, Australia.
  • Enrico De Martino
    Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Gistrup, North Jutland, Denmark.
  • Janet Millner
    Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
  • Gervase Tuxworth
    School of Information and Communication Technology, Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia.

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