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Abstract #1398

Fully automatic segmentation of all lower body muscles from high resolution MRI using a two-step DCNN model

Anudeep Konda1, Katherine Crump1, Daniel Podlisny1, Craig H Meyer1, Silvia S Blemker1, Joe Hart1, and Xue Feng1

1Springbok, Inc., Charlottesville, VA, United States

Lower limb skeletal muscles play an essential role in athletic performance as wellas muscular health in patients with dystrophies. Quantitative mapping of all 35 lower body muscles from high resolution MRI has the potential to improve power and agility in athletes and assist the diagnosis and follow-up for certain musculardystrophies in medical applications. However, due to the weak contrast and insufficient boundary information, the accurate segmentation of each individual muscle is challenging. In this study we developed a fully automatic segmentation framework using a two-step DCNN model and showed accurate segmentation for all muscles.

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