A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative

We present a multistage method for deep semantic segmentation of bone structures based on a landmark-based shape regression and subsequent local segmentation of relevant areas. Our solution covers the entire pipeline from 2D-based pre-segmentation, a method for fast deep 3D shape regression and subsequent patch-based 3D semantic segmentation for final segmentation. Since we perform landmark regression using a statistical shape model, our method is able to fit an arbitrary number of landmarks without increase in model complexity. The algorithm is evaluated on the OAI-ZIB dataset, for which we use the binary masks to generate sets of corresponding landmarks and build a deep statistical shape model. By employing our proposed deep shape fitting, our method achieves the performance of existing high-precision approaches in terms of segmentation accuracy while at the same time drastically reducing computational complexity and improving runtime by a large margin.