ArticleSkeletal muscle2024
Fully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python.
Article in Skeletal muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The association between infrapatellar fat pad volume and knee pain in non-obese postmenopausal women.Osteoarthritis imaging · 2026Article
- Clinical Sensitivity of Sub-millimetre-resolution Definite Versus Thin Inter-/Intramuscular Fat Streaks from Calf and Thigh MRI.Calcified tissue international · 2026Article
- Preoperative systemic inflammation and muscle fatty infiltration are prognostic factors for quadriceps atrophy following anterior cruciate ligament reconstruction.Frontiers in immunology · 2026Article
- Evaluation of Subcutaneous and Intermuscular Adipose Tissues by Application of Pattern Recognition and Neural Networks to Ultrasonic Data: A Model Study.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
8 authors.
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Abstract
backgroundINTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.
methodsWe detail an open-source fully-automated iterative threshold-seeking algorithm (ITSA) for segmenting IMF from T1-weighted MRI of the calf and thigh within three cohorts (CaMos Hamilton (N = 54), AMBERS (N = 280), OAI (N = 105)) selecting adults 45-85 years of age. Within the CaMos Hamilton cohort, same-day and 1-year repeated images (N = 38) were used to evaluate short- and long-term precision error with root mean square coefficients of variation; and to validate against semi-automated segmentation methods using linear regression. The effect of algorithmic improvements to fat ascertainment using 3D connectivity and partial volume correction rules on analytical precision was investigated. Robustness and versatility of the algorithm was demonstrated by application to different MR sequences/magnetic strength and to calf versus thigh scans.
resultsAmong 439 adults (319 female(89%), age: 71.6 ± 7.6 yrs, BMI: 28.06 ± 4.87 kg/m
conclusionsThe greatest challenge in segmenting IMF from MRI is in selecting a dynamic threshold that consistently performs across repeated imaging. Fully-automated ITSA achieved this, demonstrated low short- and long-term precision error, conducive of use within RCTs.
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