![]() Yields superior performance in knee cartilage defect assessment, plus itsĬonvenient 3D visualization for interpretability. Our comprehensive experiments show that the proposed method At 0 deg knee flexion, medial and lateral compartment articular cartilage contact area averaged 447 mm 2 and 265 mm 2, respectively. Then, guided by the cartilage graph representation, we design a non-Euclideanĭeep learning network with the self-attention mechanism, to extract cartilageįeatures in the local and global, and to derive the final assessment with a A mean external load of 62 N was applied in these studies. Two methodologies (MPC registration in isolation and MPC registration combined with CPC kinematics) could apply the knee joint kinematics to the static bone and cartilage surfaces in order to calculate contact kinematics. of contact using established methods24, 25, implemented in PostView. Representation, which is capable of handling highly diverse clinical data. Quantifying cartilage contact kinematics. Labrum load support, and labrum and acetabular cartilage contact stress and contact. ![]() We performed quantitative evaluations by the intensity of the fluorescent image of the degenerated area of the articular cartilage in vitro. The cartilages structure and appearance from knee MRI into a graph We investigated the effectiveness of the penetration of CNDs of various sizes into the articular cartilage of various degrees of degeneration affected by osteoarthritis. Therefore, this study is focused on combination of these two experimental. Only a small portion of voxels in knee MRI can contribute to the cartilageĭefect assessment heterogeneous scanning protocols further challenge theįeasibility of the CNNs in clinical practice the CNN-based knee cartilageĮvaluation results lack interpretability. There are no works dealing with simultaneous measurement of friction and visualization of cartilage contact. May hinder such efforts: the cartilage is a thin curved layer, implying that However, the physiologic characteristics of the cartilage Knee cartilage defect assessment by applying convolutional neural networks Engineering strain in the acetabular labrum and cartilage was computed using the distance variables shown in Fig. bone The remaining cartilage wears down faster, and eventually, the cartilage in some regions may disappear altogether, leaving the bones to rub against one another during motion. Contact analysis Kinematics were applied to each of the surface meshes using PostView, such that soft tissue structures moved rigidly with contiguous bones. In this way, many attempts have been made on ability of the cartilage to work as a shock-absorber to reduce the impact of stress on the joints. Thus earlyĭetection and assessment for knee cartilage defects are important for Knee OA, which are visible by magnetic resonance imaging (MRI). well as the other members (past and present) of the cartilage tissue engineering group. ![]() Cartilage defects are regarded as major manifestations of Download a PDF of the paper titled Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution, by Zixu Zhuang and 10 other authors Download PDF Abstract: Knee osteoarthritis (OA) is the most common osteoarthritis and a leadingĬause of disability. ![]()
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