Related Experiment Video
Updated: May 8, 2025
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Fully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and
Kenneth Tam1,2, Si Wen Liu2,3, Sarah Costa3
1Department of Neurobiology, Physiology, and Behavior, University of California Davis, Davis, CA, USA.
Background:
INTER- 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.
Methods:
We 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.
Results:
Among 439 adults (319 female(89%), age: 71.6 ± 7.6 yrs, BMI: 28.06 ± 4.87 kg/m2, IMF%: 10.91 ± 4.57%), fully-automated ITSA performed well across MR sequences and anatomies from three cohorts. Applying both 3D connectivity and partial volume fat correction improved precision from 4.99% to 2.21% test-retest error. Validation against semi-automated methods showed R2 from 0.92 to 0.98 with fully-automated ITSA routinely yielding more conservative computations of IMF volumes. Quality control shows 7% of cases requiring manual correction, primarily due to IMF merging with subcutaneous fat. A full workflow described methods to export tags for manual correction.
Conclusions:
The 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.
