  Knee osteoarthritis (OA) is the most common osteoarthritis and a leading
cause of disability. Cartilage defects are regarded as major manifestations of
knee OA, which are visible by magnetic resonance imaging (MRI). Thus early
detection and assessment for knee cartilage defects are important for
protecting patients from knee OA. In this way, many attempts have been made on
knee cartilage defect assessment by applying convolutional neural networks
(CNNs) to knee MRI. However, the physiologic characteristics of the cartilage
may hinder such efforts: the cartilage is a thin curved layer, implying that
only a small portion of voxels in knee MRI can contribute to the cartilage
defect assessment; heterogeneous scanning protocols further challenge the
feasibility of the CNNs in clinical practice; the CNN-based knee cartilage
evaluation results lack interpretability. To address these challenges, we model
the cartilages structure and appearance from knee MRI into a graph
representation, which is capable of handling highly diverse clinical data.
Then, guided by the cartilage graph representation, we design a non-Euclidean
deep learning network with the self-attention mechanism, to extract cartilage
features in the local and global, and to derive the final assessment with a
visualized result. Our comprehensive experiments show that the proposed method
yields superior performance in knee cartilage defect assessment, plus its
convenient 3D visualization for interpretability.
