Quantitative assessment of thigh muscle morphology and fatty infiltration (FF%) in post-traumatic osteoarthritis is limited. In this work, we developed a deep learning based accurate segmentation method for muscles, bone and adipose tissue from thigh MRI and used these segmentation for automated quantification of FF and cross sectional area(CSA) of these tissues. 16 patients at 10 years after ACL reconstruction were studied. The proposed method showed significant improvement in segmentation metrics (Dice, Average surface distance (ASD)) and CSA compared with popular U-Net based deep learning models. For CSA and FF% quantification, automated methods had similar measurements compared with manual segmentation.
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