A multi-institute automated segmentation evaluation on a standard dataset: Findings from the international workshop on osteoarthritis imaging segmentation challenge

Purpose: Changes in cartilage thickness are predictive of radiographic joint-space loss and joint arthroplasty. While manual segmentation is the gold-standard for evaluating cartilage morphology, it is time-consuming and has high inter-reader variability. Advances in deep-learning and convolutional neural networks (CNNs) are promising for automatic tissue segmentation, however, the heterogeneity of datasets used for network evaluation have limited pervasive utilization of these techniques. To address these limitations, a segmentation challenge was organized at the 2019 International Workshop on Osteoarthritis Imaging (IWOAI).