Deep-Learning-Based Knee Articular Cartilage Morphometrics

We proposed a deep-learning-based system for automatic knee articular cartilage morphometrics. It produces regional metrics including full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. The proposed system comprises deep learning models and algorithms that work collaboratively. We have trained convolutional neural networks for tissue segmentation, template construction, and image registration. We designed modules and pipelines for cartilage thickness mapping, cartilage lesion quantification, and cartilage parcellation. Results shows superior accuracy of the thickness mapping method and robustness of the cartilage parcellation method. The proposed FCL estimation method filled the gap in automatic cartilage lesions quantification.