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Abstract #1384

Automated segmentation of the cartilage from high-resolution isotropic T1rho MRI

Henry Rusinek1, Rahman Baboli2, Artem Mikheev2, Azadeh Sharafi2, and Ravinder R Regatte2

1Radiology, New York University School of Medicine, New York, NY, United States, 2Radiology, New York Unversity School of Medicine, New York, NY, United States

We analyze the accuracy of atlas-based cartilage segmentation from isotropic T1ρ MRI and compare it to semi-automated "seed and blanket" method and manual segmentation (ground truth). Reference 3D cartilage masks were taken as the consensus of two human experts. For patella, our implementation of template matching yielded the root mean square volume measurement error RMSE of 0.66 cm3, with interclass correlation coefficient (ICC) = 0.765 and sufficient precision to detect the gender effect. Over two-fold improvement in accuracy, RMSE = 0.25 cm3 and ICC = 0.960 was achieved with a fast, semi-automated algorithm. Similar results hold for the accuracy of the average thickness of segmented masks.

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