There are several approaches to automatic knee MRI segmentation. Some methods are directly hierarchical starting with a bone segmentation that then aids the (more difficult) cartilage segmentation with features for distance-from-bone or position-relative-to-bone.18,36,38–42 Other methods achieve a similar coarse-to-fine effect by applying atlas-based registration before either nonrigid registration21,40,43 and/or classifier-based segmentation.10,22,43 In general, it appears that integration of global information and local features is essential for solving the challenging problem of segmentation of cartilage on the background of multiple other tissue types. This explicitly or implicitly allows a zoom to several, simpler, local segmentation tasks such as cartilage versus subchondral bone, cartilage versus meniscus, cartilage versus cartilage, and cartilage versus synovial fluid. Due to differences in populations, scanners, sequences, and particularly disease stage, it is challenging to compare segmentation performances across these publications. However, it would appear that tibial/femoral cartilage Dice volume overlaps around 0.85 are attainable with these automatic methodologies.