Hess, Hanspeter; Herren, Michael; Gerber, Nicolas; Scheidegger, Olivier; Schär, Michael; Daneshvar, Keivan; Zumstein, Matthias A.; Gerber, Kate (11 June 2022). Automatic quantification of fatty infiltration of the supraspinatus from MRI. In: CAOS 2022: The 21st Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery. 10.29007/xq8m
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Automatic_quantification_of_fatty_infiltration_of_the_supraspinatus_from_MRI_pub_.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (715kB) |
Fat fraction of the rotator cuff muscles has been shown to be a predictor of rotator cuff repair failure. In clinical diagnosis, fat fraction of the affected muscle is typically assessed visually on the oblique 2D Y-view and categorized according to the Goutallier scale on T1 weighted MRI. To enable a quantitative fat fraction measure of the rotator cuff muscles, an automated analysis of the whole muscle and Y-view slice was developed utilizing 2-point Dixon MRI. 3D nn-Unet were trained on water only 2-point Dixon data and corresponding annotations for the automatic segmentation of the supraspinatus, humerus and scapula and the detection of 3 anatomical landmarks for the automatic reconstruction of the Y-view slice. The supraspinatus was segmented with a Dice coefficient of 90% (N=24) and automatic fat fraction measurements with a difference from manual measurements of 1.5 % for whole muscle and 0.6% for Y-view evaluation (N=21) were observed. The presented automatic analysis demonstrates the feasibility of a 3D quantification of fat fraction of the rotator cuff muscles for the investigation of more accurate predictors of rotator cuff repair outcome.
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