Multi-Muscle Deep Learning Segmentation to Automate the Quantification of Muscle Fat Infiltration in Cervical Spine Conditions

Abstract

Muscle fat infiltration (MFI) is observed in participants with cervical spine disorders using magnetic resonance imaging. The quantification of MFI using magnetic resonance imaging (MRI) requires time-consuming and rater-dependent manual segmentation techniques. Convolutional neural network (CNN) models have demonstrated state-of-the-art performance in a single cervical spine muscle segmentation task in participants with whiplash injury following a motor vehicle collision (MVC). Here, we expanded this work and trained a CNN for multi-muscle segmentation and automatic MFI calculation of seven cervical spine muscle groups (left and right muscles segmented separately, 14 muscles total) from high-resolution fat-water imaging in participants following a MVC (34 training datasets, n = 17, 14 females, 3 males, age = 33.7 ± 11.4 years). First, we demonstrate high test reliability and accuracy of the CNN MFI measures compared to manual segmentation in an independent testing dataset (n = 18, 11 females, 7 males, age = 31.7 ± 9.6 years). Then in 84 participants imaged within two weeks following a MVC (61 females, 23 males, age = 34.2 ± 10.7 years), we examined MFI across the muscle groups and explored the relationship between MFI and sex, age, and body mass index (BMI). Averaging across all muscle groups, females had significantly higher MFI than males (p 0.300, p