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Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).
Alternative treatment methods for knee osteoarthritis (OA) are in demand, to delay the young (< 50 Years) patient’s need for osteotomy or knee replacement. Novel interpositional knee spacers shape based on statistical shape model (SSM) approach and made of polyurethane (PU) were developed to present a minimally invasive method to treat medial OA in the knee. The implant should be supposed to reduce peak strains and pain, restore the stability of the knee, correct the malalignment of a varus knee and improve joint function and gait. Firstly, the spacers were tested in artificial knee models. It is assumed that by application of a spacer, a significant reduction in stress values and a significant increase in the contact area in the medial compartment of the knee will be registered. Biomechanical analysis of the effect of novel interpositional knee spacer implants on pressure distribution in 3D-printed knee model replicas: the primary purpose was the medial joint contact stress-related biomechanics. A secondary purpose was a better understanding of medial/lateral redistribution of joint loading. Six 3D printed knee models were reproduced from cadaveric leg computed tomography. Each of four spacer implants was tested in each knee geometry under realistic arthrokinematic dynamic loading conditions, to examine the pressure distribution in the knee joint. All spacers showed reduced mean stress values by 84–88% and peak stress values by 524–704% in the medial knee joint compartment compared to the non-spacer test condition. The contact area was enlarged by 462–627% as a result of the inserted spacers. Concerning the appreciable contact stress reduction and enlargement of the contact area in the medial knee joint compartment, the premises are in place for testing the implants directly on human knee cadavers to gain further insights into a possible tool for treating medial knee osteoarthritis.