Intramuscular fat is an important biomarker for knee osteoarthritis. Quantitative analysis on routine clinical imaging (T1-weighted MRI) is not feasible without pixel-level annotation, leading to the adoption of Goutallier classification, a semi-quantitative grading system that is time-consuming and has variable reproducibility. This study automates binarized Goutallier classification on patients (n=50) from the Osteoarthritis Initiative cohort with a two-staged process: deep-learning 3D segmentation of quadriceps and hamstrings (dice scores of 0.89[0.88,0.90] and 0.84[0.83,0.87], respectively) followed by histogram features for classification of intramuscular fat (0.93[0.92,0.95] AUROC). With model-reader kappa (0.64[0.61,0.68]) comparable to inter-reader kappa (0.61[0.59,0.64]), our approach shows promise for end-to-end automation.
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