Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.

ABSTRACT:

Background

Accurate thigh muscle segmentation from magnetic resonance imaging (MRI) enables quantitative assessment of muscle health. Although manual segmentation is the gold standard, it is labor-intensive and variable, and existing automated/semi-automatic approaches remain limited by segmentation errors/user dependence, restricting scalability. Defining data requirements for robust automated segmentation therefore remains a critical unmet need.

Purpose

To determine the number of annotated lower extremity MRI studies needed to train an accurate deep learning (DL) model for thigh muscle segmentation and to assess the effect of training size on agreement of downstream quantitative measures.

Materials and methods

Lower extremity MR images were obtained from competitive

SUBMITTER: Slutsky-Ganesh AB 

PROVIDER: S-EPMC12906233 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature