The aim of this study was to reduce time-to-segmentation for analysis of muscle quality across a large volume of the hip abductors in individuals with hip osteoarthritis by developing an automatic segmentation network. The gluteus medius (GMED), gluteus minimus (GMIN), and tensor fasciae latae (TFL) were manually segmented on fat-water separated IDEAL MR images in 44 subjects. A 3D V-Net was trained, validated, and tested using these manually segmented image volumes and resulted in a mean Dice coefficient of 0.94, 0.87, and 0.91 for GMED, GMIN, and TFL. The automatic segmentation network demonstrated strong performance and provides drastically reduced time-to-segmentation.
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