UCSF

Statistical shape modeling has been employed to study three-dimensional bony morphological features of the tibia and femur as potential risk factors for ACL injury and negative outcomes after ACL reconstruction. However, prior studies have been limited in size, largely due to the need for either CT imaging or high-resolution MRI with tedious manual segmentation. In this study, a deep learning model was trained to automatically segment tibia and femur bones from clinical MRI scans. The model was used to infer segmentations from a large dataset (> 300 images) of preoperative and postoperative clinical MR images from patients who had underwent ACL reconstruction and had clinical, two-dimensional PD-weighted MRIs. Three-dimensional bone shape models were constructed from inferred segmentations. PCA was performed, and results were compared between datasets of same knees imaged 6 months apart. Correlations between same knee principal components were moderate to strong, and point-to-point deviations between same knee vertices were small, indicating that reliable and repeatable statistical shape modeling can be obtained from clinical MRI sequences.