Rotator cuff tears (RCT) are one of the most common sources of shoulder pain. Many factors can be considered to choose the right surgical treatment procedure. Of the most important factors are the tear retraction and tear width, assessed manually on preoperative MRI.A novel approach to automatically quantify a rotator cuff tear, based on the segmentation of the tear from MRI images, was developed and validated. For segmentation, a neural network was trained and methods for the automatic calculation of the tear width and retraction from the segmented tear volume were developed.The accuracy of the automatic segmentation and the automated tear analysis were evaluated relative to manual consensus segmentations by two clinical experts. Variance in the manual segmentations was assessed in an interrater variability study of two clinical experts.The accuracy of the tear retraction calculation based on the developed automatic tear segmentation was 5.3 mm ± 5.0 mm in comparison to the interrater variability of tear retraction calculation based on manual segmentations of 3.6 mm ± 2.9 mm.These results show that an automatic quantification of a rotator cuff tear is possible. The large interrater variability of manual segmentation-based measurements highlights the difficulty of the tear segmentations task in general.