Fully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python.

ABSTRACT:

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 segment

SUBMITTER: Tam K 

PROVIDER: S-EPMC11674188 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature