Automatic 3-dimensional analysis of posterosuperior full-thickness rotator cuff tear size on magnetic resonance imaging.

Background: Tear size and shape are known to prognosticate the efficacy of surgical rotator cuff (RC) repair; however, current manual measurements on magnetic resonance images (MRIs) exhibit high interobserver variabilities and exclude 3-dimensional (3D) morphologic information. This study aimed to develop algorithms for automatic 3D analyses of posterosuperior full-thickness RC tear to enable efficient and precise tear evaluation and 3D tear visualization.

Methods: A deep-learning network for automatic segmentation of the tear region in coronal and sagittal multicenter MRI was trained with manually segmented (consensus of 3 experts) proton density- and T2-weighted MRI of shoulders with full-thickness posterosuperior tears (n = 200). Algorithms for automatic measurement of tendon retraction, tear width, tear area, and automatic Patte classification considering the 3D morphology of the shoulder were implemented and evaluated against manual segmentation (n = 59). Automatic Patte classification was calculated using automatic segmented humerus and scapula on T1-weighted MRI of the same shoulders.

Results: Tears were automatically segmented, enabling 3D visualization of the tear, with a mean Dice coefficient of 0.58 ± 0.21 compared to an interobserver variability of 0.46 ± 0.21. The mean absolute error of automatic tendon retraction and tear width measurements (4.98 ± 4.49 mm and 3.88 ± 3.18 mm) were lower than the interobserver variabilities (5.42 ± 7.09 mm and 5.92 ± 1.02 mm). The correlations of all measurements performed on automatic tear segmentations compared with those on consensus segmentations were higher than the interobserver correlation. Automatic Patte classification achieved a Cohen kappa value of 0.62, compared with the interobserver variability of 0.56. Retraction calculated using standard linear measures underestimated the tear size relative to measurements considering the curved shape of the humeral head, especially for larger tears.

Conclusion: Even on highly heterogeneous data, the proposed algorithms showed the feasibility to successfully automate tear size analysis and to enable automatic 3D visualization of the tear situation. The presented algorithms standardize cross-center tear analyses and enable the calculation of additional metrics, potentially improving the predictive power of image-based tear measurements for the outcome of surgical treatments, thus aiding in RC tear diagnosis, treatment decision, and planning.