Quantitative magnetic resonance imaging enables quantitative assessment of the healing anterior cruciate ligament or graft post-surgery, but its use is constrained by the need for time consuming manual image segmentation. The goal of this study was to validate a deep learning model for automatic segmentation of repaired and reconstructed anterior cruciate ligaments. We hypothesized that (1) a deep learning model would segment repaired ligaments and grafts with comparable anatomical similarity to intact ligaments, and (2) automatically derived quantitative features (i.e., signal intensity and volume) would not be significantly different from those obtained by manual segmentation. Constructive Interference in Steady State sequences were acquired of ACL repairs (n = 238) and grafts (n = 120). A previously validated model for intact ACLs was retrained on both surgical groups using transfer learning. Anatomical performance was measured with Dice coefficient, sensitivity, and precision. Quantitative features were compared to ground truth manual segmentation. Automatic segmentation of both surgical groups resulted in decreased anatomical performance compared to intact ACL automatic segmentation (repairs/grafts:​ Dice coefficient = .80/.78, precision = .79/.78, sensitivity = .82/.80), but neither decrease was statistically significant (Kruskal-Wallis:​ Dice coefficient p = .02, precision p = .09, sensitivity p = .17;​ Dunn post-hoc test for Dice coefficient:​ repairs/grafts p = .054/.051). There were no significant differences in quantitative features between the ground truth and automatic segmentation of repairs/grafts (0.82/2.7% signal intensity difference, p = .57/.26;​ 1.7/2.7% volume difference, p = .68/.72). The anatomical similarity performance and statistical similarities of quantitative features supports the use of this automated segmentation model in quantitative magnetic resonance imaging pipelines, which will accelerate research and provide a step towards clinical applicability.